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  • Systematic Review
  • Open Access

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review

Sports Medicine - Open20184:53

https://doi.org/10.1186/s40798-018-0167-7

  • Received: 31 July 2018
  • Accepted: 24 October 2018
  • Published:

Abstract

Background

Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties.

Methods

A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle.

Results

Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy.

Conclusion

Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error <  5°). Additional research and standardisation is required to guide clinical application.

Trial Registration

This systematic review was registered with PROSPERO (CRD42017059935).

Keywords

  • Kinematics
  • Wearable sensor
  • Inertial movement unit
  • Joint angle
  • Motion analysis
  • Upper limb

Key Points

  • Both commercially available and custom wearable sensors have some evidence of validity in the literature. Although commercial wearable sensors were validated against pseudo gold standards, each study customised the commercial software to do so.

  • Wearable sensors demonstrated errors < 5° for all degrees of freedom at the wrist and elbow joints when compared to a robotic device. The range in error is greater when measured in vivo and compared to a pseudo gold standard.

  • The measured errors are within margins that warrant future use of wearable sensors to measure joint angle in the upper limb.

Background

Clinicians and researchers seek information about the quality and quantity of patients’ movement as it provides useful information to guide and evaluate intervention. Range of motion (ROM), defined as rotation about a joint, is measured in a variety of clinical populations including those with orthopaedic, musculoskeletal, and neurological disorders. Measurement of ROM forms a valuable part of clinical assessment; therefore, it is essential that it is completed in a way that provides accurate and reliable results [1, 2].

In clinical practice, the goniometer is a widely used instrument to measure ROM [24]. Despite being considered a simple, versatile, and an easy-to-use instrument, reports of reliability and accuracy are varied. Intra-class correlation coefficients (ICCs) range from 0.76 to 0.94 (intra-rater) [3, 4] and 0.36 to 0.91 (inter-rater) [4] for shoulder and elbow ROM. Low inter-rater reliability is thought to result from the complexity and characteristics of the movement, the anatomical joint being measured, and the level of assessor experience [5, 6]. The goniometer is also limited to measuring joint angles in single planes and static positions; thus, critical information regarding joint angles during dynamic movement cannot be measured.

In research settings, three-dimensional motion analysis (3DMA) systems, such as Vicon (Vicon Motion Systems Ltd., Oxford, UK) and Optitrack (NaturalPoint, Inc., Corvallis, OR, USA), are used to measure joint angles during dynamic movement in multiple degrees of freedom (DOF). Such systems are considered the ‘gold standard’ for evaluating lower limb kinematics, with a systematic review reporting errors < 4.0° for movement in the sagittal plane and < 2.0° in the coronal plane; higher values have been reported for hip rotation in the transverse plane (range 16 to 34°) [7]. Measurement in the upper limb is considered more technically challenging due to the complexity of shoulder, elbow, and wrist movements [8]. However, given the demonstrated accuracy in the lower limb, 3DMA systems are used as the ‘ground truth’ when validating new upper limb measurement tools [9]. However, 3DMA does have limitations. Most notably, these systems are typically immobile, expensive, require considerable expertise to operate, and therefore rarely viable for use with clinical populations [10, 11].

Wearable sensors, or inertial measurement units, are becoming increasingly popular for the measurement of joint angle in the upper limb [12]. In this review, we were interested in wearable sensors that contained accelerometers and gyroscopes, with or without a magnetometer, to indirectly derive orientation. The software typically utilised three main steps: (i) calibration, using two approaches: (1) system, also referred to as ‘factory calibration’ (offset of the hardware on a flat surface), and (2) anatomical calibration including both static (pre-determined pose) and dynamic (pre-determined movement) [10, 13]; (ii) filtering, using fusion algorithms including variations of the Kalman filter (KF) [14, 15]; and (iii) segment and angle definition, using Euler angle decompositions and/or Denavit-Hartenberg Cartesian coordinates.

Wearable sensors are an increasingly popular surrogate for laboratory-based 3DMA due to their usability, portability, size, and cost. Systematic reviews have detailed their use during swimming [16] and whole body analysis [17] and in the detection of gait parameters and lower limb biomechanics [18]. However, their validity and reliability must be established and acceptable prior to their application [19]. Accuracy of the wearable sensors is dependent on the joint and movement being measured; therefore, a systematic review specific to the upper limb is required. This study aimed to establish the evidence for the use of wearable sensors to calculate joint angle in the upper limb, specifically:
  1. i.

    What are the characteristics of commercially available and custom designed wearable sensors?

     
  2. ii.

    What populations are researchers applying wearable sensors for and how have they been used?

     
  3. iii.

    What are the established psychometric properties for the wearable sensors?

     

Methods

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [20] and registered with the International Prospective Register of Systematic Reviews on 23 March 2017 (CRD42017059935).

Search Terms and Data Bases

Studies and conference proceedings were identified through searches in scientific data bases relevant to the fields of biomechanics, medicine, and engineering, from their earliest records to November 1, 2016: MEDLINE via PROQUEST, EMBASE via OVID, CINAHL via EBSCO, Web of Science, SPORTDiscus, IEEE, and Scopus. Reference lists were searched to ensure additional relevant studies were identified. The search was updated on 9 October 2017 to identify new studies that met the inclusion criteria.

The following search term combinations were used: (“wearable sens*”OR “inertial motion unit*” OR “inertial movement unit*” OR “inertial sens*” OR sensor) AND (“movement* analysis” OR “motion analysis*” OR “motion track*” OR “track* motion*” OR “measurement system*” OR movement) AND (“joint angle*” OR angle* OR kinematic* OR “range of motion*”) AND (“upper limb*” OR “upper extremit*” OR arm* OR elbow* OR wrist* OR shoulder* OR humerus*). Relevant MeSH terms were included where appropriate, and searches were limited to title, abstract, and key words. All references were imported into Endnote X6 (Thomson Reuters, Carlsbad, CA, USA), and duplicates were removed.

Study Selection Criteria and Data Extraction

The title and abstracts were screened independently by two reviewers (CW and AC). Full texts were retrieved if they met the inclusion criteria: (i) included human participants and/or robotic devices, (ii) applied/simulated use of wearable sensors on the upper limb, and (iii) calculated an upper limb joint angle. The manuals of commercial wearable sensors were located, with information extracted when characteristics were not reported by study authors. Studies were excluded based on the following criteria: (i) used a single wearable sensor, (ii) included different motion analysis systems (i.e. WiiMove, Kinetic, and smart phones), (iii) used only an accelerometer, (iv) calculated segment angle or position, (v) studied the scapula, or (vi) were not published in English.

Two reviewers (CW and AC) extracted data independently to a customised extraction form. Discrepancies were discussed, and a third reviewer (TG) was involved when consensus was not reached. Extracted parameters of the wearable sensor characteristics included custom and commercial brands, the dimensions (i.e. height and weight), components used (i.e. accelerometer, gyroscope, and magnetometer), and the sampling rate (measured in hertz (Hz)). Sample characteristics included the number of participants, their age, and any known clinical pathology. To determine if authors of the included studies customised aspects of the wearable sensors system, the following parameters were extracted: the type of calibration (i.e. system and anatomical), the fusion algorithms utilised, how anatomical segments were defined, and how joint angle was calculated.

To understand the validity and reliability of the wearable sensors, information about the comparison system, marker placement, and psychometric properties were extracted. The mean error, standard deviation (SD), and root mean square error (RMSE) reported in degrees were extracted where possible from the validation studies. The RMSE represents the error or difference between the wearable sensor and the comparison system (e.g. 3DMA system). The larger the RMSE, the greater the difference (in degrees) between the two systems. Further, to report on the validity of the wearable sensors, studies that did not delineate error between the wearable sensor and soft tissue artefact (movement of the markers with the skin) by not using the same segment tracking were not further analysed. Reliability was assessed using ICCs, with values < 0.60 reflecting poor agreement, 0.60–0.79 reflecting adequate agreement, and 0.80–1.00 reflecting excellent agreement [21].

The following parameters were used to guide the interpretation of measurement error, with < 2.0° considered acceptable, between 2.0 and 5.0° regarded as reasonable but may require consideration when interpreting the data, and > 5.0° of error was interpreted with caution [7].

Assessment of Risk of Bias and Level of Evidence

Due to the variability between research disciplines (i.e. health and engineering) in the way that studies were reported, and the level of detail provided about the research procedures, the available assessments of risk of bias and levels of evidence were not suitable for this review. Therefore, the following criteria were used to evaluate the quality of the reporting in the included studies:
  • The aim of the study was clear and corresponded to the results that were reported.

  • The study design and type of paper (i.e. conference proceeding) were considered.

  • Number of participants included in the study was considered in relation to the COSMIN guidelines which indicate that adequate samples require 50–99 participants [19].

Results

The initial search (2016) identified 1759 studies eligible for inclusion, with an additional 432 studies identified 12 months later (2017). A total of 66 studies met the inclusion criteria (Fig. 1). Eight studies reported on the  validation against a robotic device, and 22 reported on validation against a motion analysis system with human participants. One study assessed the reliability of the wearable sensors, with the remaining 35 studies using wearable sensors as an outcome measure in an experimental design.
Fig. 1
Fig. 1

A PRISMA diagram of the search strategy

Characteristics and Placement of the Wearable Sensors

The characteristics of the wearable sensors are summarised in Table 1. A total of seven customised wearable sensors and 13 commercial brands were identified. The level of detail provided for the placement of the wearable sensors on the upper limb varied significantly, as did the mode of attachment (Table 1).
Table 1

Summary of the descriptive characteristics of the wearable sensors

Study

Brand

No. of sensors used

Dimensions (mm)

L × W × H

Weight (grams)

Wireless

Components

Sample rate (Hz)

Method of attachment

Participants

First author

Conference/full text

Population

N

Mean age ± SD (years)

Acc

Gyr

Mag

Muller et al. [22]

Full

Xsens—MTw Awinda

2

47 × 30 × 13*

16*

Y*

DS tape

Healthy

1

25

Bouvier et al. [23]

Full

Xsens—MTw

4

34.5 × 57.8 × 14.5

27

Y

60

DS tape and elastic

Healthy

10

29 ± 3.4

Robert-Lachaine et al. [24]

Full

Xsens—MVN

17

50*

N

30

Velcro

Healthy

12

26.3 ± 4.4

Robert-Lachaine et al. [25]

Full

Xsens—MVN

17

50*

N

30

Velcro

Healthy

12

26.3 ± 4.4

Eckardt et al. [26]

Full

Xsens—MVN

17

50*

N

120

Body suit

Healthy

20

20.2 ± 5.7

Eckardt et al. [27]

Full

Xsens—MVN

17

50*

N

120

Body suit

Healthy

10

23.4 ± 5.3

Alvarez et al. [28]

Full

Xsens—MTx

4

38 × 53 × 21*

30*

N

50

Velcro and elastic

Robot and healthy

1

Quinones et al. [29]

Con

Xsens—MTx

7

38 × 53 × 21*

30*

N

50

SCI

15

37.4 ± 7.3

Gil-Agudo et al. [30]

Full

Xsens—MTx

5

38 × 53 × 21*

30*

N

25

Healthy

1

30

Alvarez et al. [31]

Full

Xsens—MTx

4

40 × 55 × 22

30*

50

Elastic

Robot and healthy

2

Bai et al. [32]

Con

Xsens—MTx

3

38 × 53 × 20.9

30

N

100

Bai et al. [33]

Con

Xsens—MTx

2

38 × 53 × 21*

30*

120

Velcro

Healthy

1

Zhang et al. [34]

Full

Xsens—MTx

3

38 × 53 × 21*

30*

100

Healthy

4

Rodriques-Anglese et al. [35]

Con

Xsens—MTx

2

38 × 53 × 21*

30*

N

100

Robot and healthy

1

Cutti et al. [36]

Full

Xsens—MT9B

4

39 × 54 × 28

38

N

100

DS tape and elastic

Healthy

1

23

Zhou et al. [37]

Full

Xsens—MT9B

2

N

25

Velcro

Healthy

4

20–40

Zhou et al. [38]

Full

Xsens—MT9B

2

N

25

Healthy

1

Perez et al. [39]

Full

Xsens—MTi

4

58 × 58 × 22*

50

50

Fabric

Healthy

1

Miezal et al. [15]

Full

Xsens

3

120

Healthy

1

30

Miguel-Andres et al. [40]

Full

Xsens

3

N

75

Velcro and DS tape

Healthy

10

29.3 ± 2.21

Luinge et al. [41]

Full

Xsens

2

N

DS tape and leukoplast

Healthy

1

Morrow et al. [42]

Full

ADPM Opal

6

43.7 × 39.7 × 13.7*

< 25*

Y

80

Strap

Surgeons

6

45 ± 7

Rose et al. [43]

Full

ADPM Opal

6

43.7 × 39.7 × 13.7*

< 25*

Y

128

Strap

Surgeons

14

Bertrand et al. [44]

Con

ADPM Opal

3

48 × 36 × 13

< 22

Y

DS tape

Astronauts

2

Fantozzi et al. [45]

Full

ADPM Opal

7

43.7 × 39.7 × 13.7*

< 25*

Y

128

Velcro

Swimmers

8

26.1 ± 3.4

Kirking et al. [46]

Full

ADPM Opal

3

43.7 × 39.7 × 13.7*

22

DS tape and strap

Healthy

5

Ricci et al. [47]

Full

ADPM Opal

6

43.7 × 39.7 × 13.7*

< 25*

Y

128

Velcro

Robot

El-Gohary et al. [48]

Full

ADPM Opal

3

43.7 × 39.7 × 13.7*

< 25a

128

Velcro

Robot

Ricci et al. [49]

Con

ADPM Opal

5

43.7 × 39.7 × 13.7*

< 22

Y

128

Velcro

Healthy

4 and 4

7 ± 0.3 and 27 ± 1.9

El-Gohary et al. [50]

Full

ADPM Opal

2

43.7 × 39.7 × 13.7*

< 25*

128^

Velcro

Healthy

8

El-Gohary et al. [51]

Con

ADPM Opal

2

43.7 × 39.7 × 13.7*

< 25*

Y

Strap

Healthy

1

Mazomenos et al. [52]

Full

Shimmer 2r

2

Y

50

Custom holders and elastic

Healthy and stoke

18 and 4

25–50 and 45–73

Tran et al. [53]

Con

Shimmer 2r

2

Y

18

Strap

Healthy

1

Daunoravicene et al. [54]

Full

Shimmer

3

 

51.2

Strap

Stroke

14

60.8 ± 12.5

Bertomu-Motos et al. [55]

Full

Shimmer

2

51 × 34 × 14*

Y

Strap

Healthy

4 and 50

21–51 and 20–72

Meng et al. [56]

Con

Shimmer

2

51 × 34 × 14*

Y

20

Velcro

Spherical coordinate system and healthy

1

Peppoloni et al. [57]

Con

Shimmer

3

51 × 34 × 14*

 

Y

100

Velcro

Healthy

1

Ruiz-Olaya et al. [58]

Full

InvenSense

MPU9150 chip

2

N

50

Straps

Healthy

3

Callejas –Curervo et al. [59]

Full

InvenSense

MPU9150 chip

2

N

30

DS tape

Robot and healthy

3

Li et al. [60]

Full

InvenSense MPU9150 chip

2

N

Stroke and Healthy

35 and 11

Gao et al. [61]

Con

InvenSense

MPU9150 chip

2

26.2 × 39.2 × 14.8

Y

Healthy

1

25

Lambretcht et al. [62]

Full

InvenSense

MPU9150 chip

4

12 × 12 × 6

N

50

Healthy

1

Peppoloni et al. [63]

Con

InvenSense

MPU9150 chip

4

Velcro

Healthy

1

Eom et al. [64]

Full

InvenSense

MPU6050 chip

2

Y

Straps

Robot and goniometer

  

Roldan-Jimenez et al. [65]

Full

InterSense InertiaCube3

3

26.2 × 39.2 × 14.8

17

N

DS tape and elastic cohesive bandage

Healthy

15

18–35

Roldan-Jimenez et al. [66]

Full

InterSense

InertiaCube3

4

26.2 × 39.2 × 14.8

17

N

1000

DS tape and elastic cohesive bandage

Healthy

11

24.7 ± 4.2

Nguyen et al. [67]

Con

BioKin WMS

2

Y

200

Straps

Healthy

15

20–60

Karunarathne et al. [68]

Con

BioKin WMS

2

Y

Straps

Healthy

4

Ligorio et al. [69]

Full

YEI Technology

2

N

220

Velcro

Healthy

15

28 ± 3

Vignais et al. [70]

Full

CAPTIV Motion

5

60 × 35 × 19

32

Ya

64

Straps

Healthy

5

41.2 ± 11

Chen et al. [71]

Con

L-P Research Motion Sensor B2

8

39 × 39 × 8*

12

Y

Goniometer

Matsumoto et al. [72]

Full

Noraxon Myomotion

13

37.6 × 52 × 18.1

< 34

200

Healthy and stoke

10 and 1

32.2 ± 9.3 and 27

Schiefer et al. [73]

Full

CUELA

13

50

Velcro

Healthy

20

37.4 ± 9.9

Balbinot et al. [74]

Full

ArduMuV3 chip

9

Y

20

Straps

Huang et al. [75]

Full

MSULS

4

30 × 35 × 12

50

Fabric

Healthy and stoke

11 and 22

53 ± 8 and 62 ± 10

Salam et al. [76]

Full

Custom

3

44.45 × 44.45

Y

150

Cricketers

10

Chang et al. [77]

Full

Custom

2

N

Robot

Borbely et al. [78]

Con

Custom

2

N

200

Velcro

1

Kumar et al. [79]

Full

Custom

14

66.6 × 28.2 × 18.1*

22*

Y*

25

Custom holders and Velcro

Healthy and un-healthy

19 and 19

24.6 ± 6.7 and 68.4 ± 8.9

Lee et al. [80]

Full

Custom

7

66.6 × 28.2 × 18.1

22

Y

25

Straps

Goniometer and stroke

5

68

Cifuentes et al. [81]

Con

Custom

2

43 × 60

60

Straps

Healthy

9

Kanjanapas et al. [82]

Full

Custom

2

N

100

Orthosis

Healthy

1

25

Zhang et al. [83]

Con

2

Y

Healthy

1

Lin et al. [84]

Full

2

Y

Straps

Stroke

25

52.2 ± 10.2 and 62.2 ± 7.1

El-Gohary et al. [85]

Con

2

Hyde et al. [86]

Full

Robot

Table 1 is organised by the brand of the wearable sensor followed by the date that the study was published. This allows direct comparison to be made within the brand of the wearable sensors and trends to be identified between more recently published studies

Abbreviations: Gms grams, Y yes, N no, Acc accelerometer, Gyr gyroscope, Mag magnetometer, Hz hertz (unit of frequency), SD standard deviation, SCI spinal cord injury, PD Parkinson’s disease, Full full text, Con conference paper, mm millimetre, DS double sided

Key:

Wireless—the wearable sensor system was considered wireless if the wearable sensors did not have wires connecting them to an external source, even if that external source was also mounted on the subject

Sample rate—the number of data samples collected per second by the wearable sensor measured in hertz (Hz) which is the unit of frequency

Custom—defined as a newly developed wearable sensor or modifications have occurred to the pre-existing hardware of the wearable sensor

Symbols:

*The information was obtained from the manufacturer procedure manual or other referenced papers

^The sample rate was down sampled (reduced) to allow comparison to the MOCAP system

Information was not reported and/or unclear in the study and/or unable to be obtained from the manufacturer manual

Calibration Methods

Forty-seven studies reported on a calibration procedure prior to data acquisition. System calibration, also commonly known as ‘factory calibration’, was reported on 12 occasions, with two procedures described for the wearable sensors: (i) placement on a flat surface and/or (ii) movement in a pre-determined order while attached to a flat surface [56, 62]. The aim of system calibration was reported to be to align coordinate systems [39, 56] and account for inaccuracies in the orientation of wearable sensor chip relative to its case/packaging [62]. Static anatomical calibration was performed often (n = 34), with dynamic anatomical calibration performed sometimes (n = 10) [23, 30, 36, 41, 45, 49, 57]. Only one study used system calibration alongside both static and dynamic anatomical calibrations to compute joint kinematics [47].

Populations Assessed Using Wearable Sensors

Most studies (n = 52) recruited healthy adults; participants with known pathology were reported in nine studies (Table 1). One study recruited children (< 18 years) [49]. Sample sizes ranged from 1 to 54 participants, with a median sample of 7.6 participants per study. Twenty-nine studies recruited less than five participants, with 20 studies recruiting one single participant.

Psychometric Properties of Wearable Sensors

Validity

Validation studies were split into two categories: (i) studies that compared the wearable sensor output to simulated upper limb movement on a robotic device (Table 2) and (ii) studies that compared wearable sensors output to a 3DMA system on a human participant (Table 3). The term ‘error’ is used to describe the difference between the capture systems; however, we acknowledge that comparisons between the wearable sensors and a robotic device are the only true measures of error.
Table 2

List of the 8 articles organised by first author and containing information related to the validation of wearable sensors for the measurement of joint angle for simulated movements of the upper limb when compared to a robotic device

First author

Aim of the study

Brand of wearable sensors

Description of robotic device

Sensor fusion algorithm

Calibration

Segment(s)

DOFs

Simulated movements

RMSE

Mean error (SD)

System

Static

Dynamic

Callejas–Cuervo et al. [59]

System validation

Invensense MPU-9150

Industrial robotic arm (ABB IRB 120)

KF

Elbow

1DOF

Flex/ext

2.12–2.44°

Chang et al. [77]

System validation

Custom

Rehabotics Medical Technology Corporation

Finger

1DOF

Flex/ext

5–7°

Alvarez et al. [28]

System validation

Xsens

Pan and tilt unit (Model PTU-D46)

Wrist

2DOF

Flex

Lat dev

0.06° (9.20)

1.05° (2.18)

Alvarez et al. [31]

System validation

Xsens

Pan and tilt unit (Model PTU-D46)

Wrist

2DOF

Flex

Lat dev

1.8° for each axis, with a max error ± 6°

Rodriguez-Angleseet et al. [35]

System validation

Xsens

Plantar robot

KF

Elbow

2DOF

Did not report discrete statistics

Kirking et al. [46]

Validation/comparison of sensor fusion methods

Opal

Industrial Epson C3 robot arm

UKF

Shoulder

Elbow

Forearm

Wrist

2DOF

1DOF

1DOF

2DOF

Int/ext rot

Flex/ext

Flex/ext

Pro/sup

Flex/ext

Twist

8.1°

2.4°

2.6°

2.1°

2.2°

3.9°

Modified UKF

Shoulder

Elbow

Forearm

Wrist

2DOF

1DOF

1DOF

2DOF

Int/ext rot

Flex/ext

Flex/ext

Pro/sup

Flex/ext

Twist

3.0°

1.6°

2.0°

1.2°

1.5°

2.8°

Ricci et al. [47]

Validation/comparison of sensor fusion methods

Opal

LWR 4+ (KUKA GmbH)

KF

 

Shoulder

Elbow

Forearm

Wrist

7DOF

Unable to determine exact values from box plot

GNF

Shoulder

Elbow

Forearm

Wrist

7DOF

El-Gohary et al. [48]

Validation/comparison of sensor fusion methods

Opal

Not described

UKF

Shoulder

Elbow

Forearm

Wrist

2DOF

1DOF

1DOF

2DOF

In/ext rot

Flex/ext

Flex/ext

Pro/sup

Flex/ext

Twist

Slow

Med

Fast

7.8°

0.8°

0.9°

1.3°

1.1°

1.7°

3.0°

1.6°

2.0°

1.2°

1.5°

2.8°

5.9°

2.5°

2.8°

1.1°

1.8°

2.2°

EKF

Shoulder

Elbow

Forearm

Wrist

2DOF

1DOF

1DOF

2DOF

In/ext rot

Flex/ext

Flex/ext

Pro/sup

Flex/ext

Twist

8.8°

1.2°

1.3°

0.8°

1.2°

1.8°

8.6°

1.9°

2.1°

1.4°

1.9°

3.7°

9.7°

2.5°

3.1°

1.4°

2.9°

3.4°

Abbreviations: RMSE root mean square error, SD standard deviation, CMC coefficient of multiple correlation, KBF Kalman-based filter, KF Kalman filter, EKF extended Kalman filter, UKF unscented Kalman filter, WLS weighted least squares, Flex flexion, Ext extension, Pro pronation, Sup supination, Ab abduction, Ad adduction, Dev deviation, Rad radial, Uln ulnar, In internal, Ex external, Rot rotation, Elev elevation, Dep depression, DOF degrees of freedom, C customised, M manufacture

–Information was not reported and/or unclear in the study and/or unable to be obtained from the manufacturer manual

Table 3

List of the selected 22 articles organised by first author and containing information related to the validation of wearable sensors for the measurement of joint angle in upper limb when compared to a three-dimensional motion analysis system

First Author

Aim of the study

Brand of Sensors

Sensor fusion algorithm

Placement of sensors

Comparison system

Used same segment tracking

Task(s)

Anatomical Segment(s)

Degrees of Freedom

Movements

Mean error (SD)

RMSE

Correlation coefficients

Calibration

System

Static

Dynamic

Robert Lachaine et al. [24]

Validate protocol

Xsens

KF

S1: Upper arm

S2: Forearm

S3: Hand

Optotrak

Yes

Elbow flex/ext, pro/sup; wrist flex/ext, ul/rad deviation, rotation and manual handling tasks

Shoulder

Elbow

Wrist

3DOF

3DOF

3DOF

Flex/ext

Ab/ad

Rotation

Flex/ext

Ab/ad

Pro/sup

Flex/ext

Rad/ul dev

Rotation

Optotrak ISB to Xsens ISB

3.0°

2.9°

2.5°

2.9°

2.0°

2.6°

3.8°

2.8°

3.6°

Ligorio et al. [69]

Validate calibration method

YEI technology

Vicon

Yes

Flex/ext and pro/sup

Elbow

2DOF

2DOF 2DOF

Flex/ext

Pro/supFlex/ext

Pro/supFlex/ext

Pro/sup

Method A

8.5–11.1°

11.9–13.3°

Method B

3.4–3.6°

6.8–7.6°

Method C – Proposed

3.1–3.3°

3.8–4.0°

Fantozzi et al. [45]

Validate protocol

Opal

KBF

S1: Flat portion of the sternum.

S2: Laterally on the humerus above the centre and posteriorly.

S3: Distal forearm above the ulnar and radial styloid.

S4: Back of the hand.

Stereo-photogrammetric system (SMART-DX 7000)

Yes

Simulated front crawl

Shoulder

Elbow

Wrist

3DOF

2DOF

2DOF

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

Flex/ext

Rad/ul dev

5.0° (4–6)

10.0° (7–11)

7.0° (5–8)

15° (12–17)

10.0° (7–11)

5.0° (4–5)

3.0° (2–4)

0.99

0.97

0.99

0.95

0.93

0.95

0.90

Simulated breaststroke

Shoulder

Elbow

Wrist

3DOF

2DOF

2DOF

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

Flex/ext

Rad/ul dev

5.0° (3–7)

3.0° (3–4)

8.0° (5–10)

6.0° (5–10)

5.0° (4–7)

4.0° (3–5)

-

0.99

0.99

0.98

0.97

0.98

0.93

Gil-Agudo et al. [30]

Validate protocol

Xsens

KF

S1: Trunk

S2: Back of the head

S3: Right arm

S4: Distal forearm

S5: Hand.

CODA

Yes

Shoulder rot, flex/ext and ab/ad; elbow flex/ext and pro/sup, wrist flex/ext and ul/rad deviation.

Shoulder

Elbow

Wrist

3DOF

2DOF

2DOF

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

Flex/ext

Rad/ul dev

0.76° (4.4)

0.69° (10.47)

0.65° (5.67)

0.54° (2.63)

5.16° (4.5)

3.47° (9.43)

2.19° (4.64)

Miezal et al. [15]

Validate sensor fusion/algorithm

Xsens

EKF, WLS

Not described

Natural Point Optitrack system 13 cameras

Yes

Eight-shaped movements at varied speeds, smooth parts imitating reaching and steering in the case of real-slow, and agile parts with quick starts and stops, as well as, parts reminding of sportive movements, such as boxing, in the case of real fast

Shoulder

Elbow

Wrist

1DOF

1DOF

1DOF

Chaintracker (real fast w/mag)

9.38° (5.79)

11.91° (6.27)

7.37° (4.60)

Shoulder

Elbow

Wrist

1DOF

1DOF

1DOF

Chaintracker (real slow w/mag)

4.76° (2.24)

8.83° (4.64)

4.72° (2.61)

Shoulder

Elbow

Wrist

1DOF

1DOF

1DOF

Optitracker (real fast w/mag)

1.88° (0.91)

2.22° (1.38)

2.28° (1.15)

Shoulder Elbow

Wrist

1DOF

1DOF

1DOF

Optitracker (real fast w/mag)

1.27° (0.81)

2.16° (1.35)

2.32° (1.37)

Lambretcht et al. [62]

Validate sensor fusion/algorithm

Custom

DMP algorithm

S1: Sternum

S2: Upper arm

S3: Distal forearm

S4: Hand

Optotrak

Yes

Reaching movements

Shoulder

Elbow

Wrist

3DOF

2DOF

2DOF

Azimuth

Elev

Int rot

Flex

Pro

Flex/Ext

Dev

4.9°

1.2°

2.9°

7.9°

1.5°

5.5°

2.6°

0.99

0.99

0.99

0.99

0.99

0.97

0.94

Zhang et al. [34]

Validate sensor fusion/algorithm

Xsens

UKF

S1: Sternum

S2: Lateral side above the elbow S3: Lateral and flat side of the forearm near the wrist

BTS SMART-D optoelectronic tracking system

Yes

Move the upper limb arbitrarily.

Shoulder

Elbow

3DOF

2DOF

Flex/ext

Ab/ad

Int/ext rot

Flex/ext

Pro/sup

Independent Estimation

0.070° (0.083)

0.023° (0.042)

0.061° (0.061)

0.052° (0.155)

0.321° (0.265)

0.11°

0.04°

0.08°

0.16°

0.41°

0.99

0.99

0.99

0.81

0.96

Shoulder

Elbow

3DOF

2DOF

Flex/ext

Ab/ad

Int/ext rot

Flex/ext

Pro/sup

Constraints method

0.040° (0.039)

0.013° (0.018)

0.029° (0.032)

0.046° (0.100)

0.155° (0.143)

0.05°

0.02°

0.04°

0.11°

0.21°

0.99

0.99

0.99

0.88

0.96

Shoulder

Elbow

3DOF

2DOF

Flex/ext

Ab/ad

Int/ext rot

Flex/ext

Pro/sup

Papers proposed method

0.028° (0.029)

0.007° (0.013)

0.035° (0.036)

0.054° (0.093)

0.168° (0.153)

0.04°

0.01°

0.05°

0.10°

0.22°

0.99

0.99

0.99

0.89

0.96

Morrow et al. [42]

Validate protocol

Opal

Bilateral:

S1: Lateral aspect upper arms

S2: Forearms

Raptor 12 Digital Real-time Motion Capture System

No

Peg transfer task using straight laparoscopic surgical instruments.

Shoulder

Elbow

1DOF

1DOF

Elevation

Flexion

3.0° (2.1)

2.2° (1.6)

6.8° (2.7)

8.2° (2.8)

Callegas-Cuerro et al. [59]

Validate protocol

Invensense MPU-9150

KF

S1: External arm aligned with the humerus.

S2: Between the radial styloid and ulnar styloid, aligned with external part of the hand.

Qualisys Oqus 5

No

Flex/ext

Elbow

1DOF

Flex/ext

< 3.0° to < 5.0°

2.44%

Meng et al. [56]

Validate protocol

Shimmer

KF

Not described

Vicon Mocap System

No

(1) Raise shoulder. (2) Move shoulder right then left. (3) Clockwise axial rotation to its max, then rotate the upper arm counter clockwise. (4) Elbow extension move into flexion.

Shoulder

Elbow

3DOF

2DOF

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

0.50° (1.79)

0.18° (1.34)

0.16° (1.96)

1.86° (1.85)

1.22° (2.87)

1.85°

1.35°

1.96°

2.62°

3.12°

Cifuentes et al. [81]

Validate protocol

Custom

S1: Arm

S2: Forearm

Optical tracking system

No

Reaching and grasping from the rest position with the forearm on the table, at angle of approximately 90° with respect to the arm before reaching and grasping an object, and then returning it to starting position.

Elbow

1DOF

Flex/ext

No discrete data reported only figures of continuous data

Muller et al. [22]

Validate sensor fusion/algorithm

Xsens

KF*

S1: Thorax.

S2: Lateral side of the arm

S3: Posterior side of the wrist

Vicon

No

(1) Flex/ext in a horizontal plane with the shoulder abducted 90° flex/ext in a sagittal plane while standing with the elbow close to the trunk. (2) Flex/ext in a sagittal plane with the spine bent forward 90° and the upper arm aligned horizontally and parallel to the ground sup/pro with the elbow flexed 90°

Elbow

Elbow

2DOF

2DOF

Flex/ext

Pro/sup

Flex/ext

Pro/sup

Proposed algorithm

2.7°

3.8°

Manual alignment

3.8°

8.7°

Bertomu-Motos et al. [55]

Validate sensor fusion/algorithm

Shimmer

EKF

S1: Shoulder

S2: Upper arm

Optitrack

No

The activity consisted of taking a box from the perimeter and placing it in the centre of the screen.

Shoulder

5DOF

Unclear

Without compensation Filter

5.24° (3.38)

0.5° (1.6)

3.6° (2.1)

1.8° (1.0)

1.60° (0.6)

Shoulder

5DOF

Unclear

Compensation filter

1.69° (2.1)

1.1° (0.8)

5.9° (2.3)

2.6° (1.7)

0.9° (1.2)

Karunarathne et al. [68]

Validate sensor fusion/algorithm

BioKin WMS

KF*

S1: Near the elbow

S2: Wrist

Vicon

No

Lifting a water bottle

Elbow

1DOF

Flex/ext

High-pass filte—gyroscope

10.18°

Elbow

1DOF

Flex/ext

Low-pass filter—accelerations

18.30°

Elbow

1DOF

Flex/ext

Tradition complementary filter

10.30°

Elbow

1DOF

Flex/ext

Adaptive complementary filter

8.77°

El-Gohary et al. [50]

Validate Sensor fusion/algorithm

Opal

UKF

S1: Upper arm

S2: Forearm

Vicon motion analysis system

No

Single movements: Shoulder flex/ext, ab/ad, Elbow flex/ext and forearm sup/pro.

Shoulder

Elbow

2DOF

2DOF

Flex/ext

Ab/ad

Flex/ext

Pro/sup

5.5°

4.4°

6.5°

0.95°

0.98

0.99

0.98

0.95

 

Complex tasks: (1) touching nose and (2) reaching for door

Shoulder

Elbow

1DOF

1DOF

9.8°

8.8°

6.5°

5.5°

0.94

0.95

El-Gohary et al. [51]

Validate Sensor fusion/algorithm

Opal

UKF

S1: Between the shoulder and elbow

S2: Near the wrist

Eagle Analog System, Motion Analysis

No

Single movements at different speeds: Shoulder flex/ext, ab/ad, Elbow flex/ext, sup/pro

Shoulder

Elbow

2DOF

2DOF

Flex/ext

Ab/ad

Flex/ext

Pro/sup

Normal speed

0.97

0.94

0.92

0.96

Shoulder

Elbow

2DOF

2DOF

Flex/ext

Ab/ad

Flex/ext

Pro/sup

Fast speed

  

0.94

0.91

0.89

0.93

Perez et al. [39]

Validate sensor fusion/algorithm

Xsens

S1: Back

S2: 18 cm from acromion

S3: 25 cm from epicondyle

S4: 5.5 cm from distal radio-cubital joint.

BTS SMART-D optoelectronic tracking system

No

Single movements: Shoulder flex/ ext, horizontal ab/ad, and internal rotation. Elbow flex, pro/sup and wrist flex/ext.

Shoulder

Elbow

Wrist

3DOF

2DOF

1DOF

Flex/ext

Ab/ad

In rot

Flex

Pro/sup

Flex/ext

13.4°

17.2°

60.4°

5.8°

24.1°

11.6°

0.99

0.71

0.99

0.98

0.96

0.98

Pouring water from a glass jar into a glass

Shoulder

Elbow

Wrist

3DOF

2DOF

1DOF

Flex/ext

Ab/ad

In rot

Flex/ext

Pro/sup

Flex/ext

13.8°

7.4°

28.8°

18.6°

11.7°

26.8°

0.99

0.90

0.85

0.97

0.92

0.92

Zhou et al. [37]

Validate sensor fusion/algorithm

Xsens

KF

S1: Lateral aspect of upper arm between the lateral epicondyle and the acromion process (5 cm from the AP)

S2: Wrist centre on the palmer aspect

CODA

No

Reaching, shrugging, forearm rotation

Elbow

2DOF

Flex/ext

Rot

0.4° (2.34)

0.06° (4.82)

2.4°

4.8°

Luinge et al. [41]

Validate sensor fusion/algorithm

Xsens

KF

S1: Lateral upper arm near the elbow

S2: Dorsal side of the forearm near the wrist.

Vicon

No

(1) Mimicking eating routines (pouring a glass eating soup, eating spaghetti, eating meat, drinking). (2) Mimicking morning routines (splashing water on face and drying it using a towel, applying deodorant, buttoning a blouse, combing hair, brushing teeth).

Elbow

2DOF

No discreet data reported

Peppoloni et al. [57]

Validate kinematic model

Shimmer

UKF

S1: Scapula beside the angulus acromialis

S2: Lateral side of the upper arm above the elbow.

S3: Lateral side of forearm a few centimetres far from the wrist.

Vicon

No

Single movements:

Scapula elev/dep, ante-position/retro-position. Shoulder flex/ext, ab/ad, and int/ext rotation. Elbow flex/ext, pro/sup.

7DOF model

Scapula

Shoulder

Elbow

2DOF

3DOF

2DOF

Elev/dep

Prof/retr

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

6.19°

3.43°

8.19°

10.68°

8.79°

5.00°

9.61°

0.65

0.74

0.94

0.63

0.97

0.99

0.85

5DOF model

Shoulder

Elbow

3DOF

2DOF

Flex/ext

Ab/ad

In/ext rot

Flex/ext

Pro/sup

7.03°

6.03°

4.95°

9.93°

11.29°

0.95

0.87

0.99

0.98

0.85

Robert-Lachaine et al. [25]

Validate calibration method

Xsens

KF

Optotrak

No

Single plane movements

No discrete data reported

Bouvier et al. [23]

Validate calibration method

Xsens

KF

S1: Sternum

S2: Central third of upper arm laterally (or slightly posterior)

S3: Dorso-distally on the forearm

S4: Dorsum hand

Eagle 4 Optoelectric system

No

Move through 9 calibration trials for each joint.

Shoulder

Elbow

Wrist

3DOF

2DOF

2DOF

Flex/ext

Ab/Ad

Wheel

Flex/ext

Pro/sup

Flex/ext

Ab/sd

20.46°

14.76°

14.21°

13.9°

0.84

0.94

0.93

0.68

Abbreviations: RMSE root mean square error, SD standard deviation, CMC coefficient of multiple correlation, KBF Kalman-based filter, KF Kalman filter, EKF extended Kalman filter, UKF unscented Kalman filter, WLS weighted least squares, Flex flexion, Ext extension, Pro pronation, Sup supination, Ab abduction, Ad adduction, Dev deviation, Rad radial, Uln ulnar, In internal, Ex external, Rot rotation, Elev elevation, Dep depression, DOF degrees of freedom, C customised, M manufacture

*The information was obtained from the manufacturer procedure manual or other referenced papers

–Information was not reported and/or unclear in the study and/or unable to be obtained from the manufacturer manual

Robot Comparisons

Eight studies reported the error of wearable sensors when compared to simulated upper limb movement on a robotic device (Table 2). A mean error between 0.06 and 1.8° for flexion and 1.05 and 1.8° for lateral deviation of the wrist was reported using Xsens [28, 31]. For elbow flexion/extension, the difference between Invensence and the robotic device was between 2.1 and 2.4° [59]. For finger flexion/extension, RMSEs ranged from 5.0 to 7.0° using a customised wearable sensor system [77].

Three studies reported the error associated with the use of different fusion algorithms. Using the unscented Kalman filter (UKF) to fuse data from Opal wearable sensors, the RMSE range was 0.8–8.1° for 2DOF at the shoulder, 0.9–2.8° for 1DOF at the elbow, 1.1–3.9° for 1DOF of the forearm, and 1.1–2.1° for 2DOF at the wrist [46, 48]. The rotation of the shoulder and twist of the wrist resulted in more error compared to single plane movements of flexion/extension and pronation/supination [46, 48]. When the UKF was compared to a modified UKF, lower RMSEs were found across all 6DOF using the modified UKF [46]. One study investigated the effects that speed of movement had on measurement error. Using Opal wearable sensors, the UKF was compared to the extended Kalman filter (EKF) under three speed conditions: slow, medium, and fast. For slow movements, both fusion algorithms were comparable across all 6DOF (RMSE 0.8–7.8° for the UKF and 0.8–8.8° for the EKF). The UKF resulted in less error across 6DOF for the medium (RMSE 1.2–3.0°) and fast (RMSE 1.1–5.9°) speeds compared to the EKF (RMSE 1.4–8.6°; 1.4–9.7°) [48].

3DMA Comparisons

Twenty-two studies compared the joint angles calculated by wearable sensors, both custom and commercial, to a ‘gold standard’ 3DMA system (Table 3). Studies that used same segment tracking (i.e. motion analysis markers directly on the wearable sensors) were reported in 7 studies. Opal wearable sensors were compared to a 3DMA system during simulated swimming (multiplane movement). The largest difference between the two systems occurred at the elbow (RMSE 6–15°), with the least occurring at the wrist (RMSE 3.0–5.0°) [45]. Xsens was compared to codamotion during single plane movement, with the addition of a dynamic anatomical calibration trial [30]. The largest difference occurred at the elbow (5.16° ± 4.5 to 0.54° ± 2.63), and the least difference at the shoulder (0.65° ± 5.67 to 0.76° ± 4.40) [30]. Xsens was compared to Optotrak with consistent differences between systems across all DOFs of the shoulder (RMSE 2.5–3.0°), elbow (RMSE 2.0–2.9°), and wrist (RMSE 2.8–3.8°) [24].

Three studies investigated the performance of wearable sensors using different fusion methods to amalgamate the data and compared this to a ‘gold standard’ system. Zhang and colleagues [34] compared the accuracy of their own algorithm to two pre-existing algorithms. Comparing Xsens to the BTS Optoelectronic system, their methodology resulted in less error (RMSE = 0.08°, CC = 0.89 to 0.99) across 5DOF compared to the two other methods [34]. The addition of a magnetometer in the analysis of data was also investigated using the EKF- and non-EKF-based fusion algorithm [15]. The latter produced the least difference between the two systems, irrespective of the speed of the movement and whether or not a magnetometer was included. In contrast, the EKF fusion algorithm resulted in the largest difference from the reference system, particularly for fast movements where magnetometer data was included (7.37° ± 4.60 to 11.91° ± 6.27) [15]. The level of customisation to achieve these results is summarised in Table 4.
Table 4

Summary of the software customisation reported by the authors for validation studies that used the same segment tracking

First author

Sensor hardware

Software

Sensor fusion algorithm

Calibration

Anatomical segment definition

Kinematic calculation

Robert Lachaine et al. [24]

Commercial—Xsens MVN

Manufacturer

Manufacturer

Custom

Custom

Ligorio et al. [69]

Commercial—YEI Technology

Custom

Custom

Custom

Custom

Fantozzi et al. [45]

Commercial—ADPM Opal

Custom

Custom

Custom

Custom

Gil-Agudo et al. [30]

Commercial—Xsens MTx

Custom

Custom

Custom

Custom

Miezal et al. [15]

Commercial—Xsens

Did not report

Did not report

Custom

Custom

Lambretcht et al. [62]

Commercial—InvenSense MPU9150 chip

Custom

Custom

Custom

Custom

Zhang et al. [34]

Commercial—Xsens MTx

Custom

Manufacturer

Custom

Custom

One study compared the difference between YEI Technology (YEI technology, Portsmouth, OH) wearable sensors and Vicon during three customised calibration methods for the elbow, which resulted in RMSEs that ranged from 3.1 to 7.6° [69].

Reliability

Adequate to excellent agreement was reported for 2DOF at the shoulder (ICC 0.68–0.81) and poor to moderate agreement for the 2DOF at the elbow (ICC 0.16–0.83). The wrist demonstrated the highest overall agreement with ICC values ranging from 0.65 to 0.89 for 2DOF [73].

Risk of Bias

The sample sizes of the included studies were mostly inadequate, with 30% including single participants (Table 1). Twenty-eight percent of the included studies were conference papers, providing limited information.

Discussion

This systematic review described the characteristics of wearable sensors that have been applied in research and clinical settings on the upper limb, the populations with whom they have been used with, and their established psychometric properties. The inclusion of 66 studies allowed for a comprehensive synthesis of information.

Similar to other systematic reviews on wearable sensors, commercial wearable sensors, as opposed to custom designed, were reported in most studies (83%) [17]. One benefit for users of commercial wearable sensors is the user-friendly nature of the associated manufacturer guidelines and processing software, including in-built fusion algorithms and joint calculation methods. However, the studies that utilised commercial hardware often customised aspects of the software (i.e. fusion algorithm, calibration method, anatomical segment definition, and the kinematic calculation). Therefore, the validity and reliability of an entirely commercial system (hardware and software) for use in the upper limb remains unknown. Customisation impacts the clinical utility of the wearable sensor systems, especially if there are no support personnel with appropriate knowledge and expertise.

Of the studies reviewed, there was no consensus on the procedures to follow for using wearable sensors on the upper limb. The placement of the wearable sensors varied and, in some cases, was poorly described. Manufacturer guidelines for placement of commercial wearable sensors were not referred to, which lead to apparent differences in placement for studies that utilised the same commercial brand. Multiple fusion algorithms were reported, with no clear outcome about which was best suited to a specific joint or movement. The level of customisation of fusion algorithms makes it difficult to compare between studies, and often, the specifics of the algorithm were not readily available, limiting replication. Similar inconsistencies and a lack of consensus were reported in other systematic reviews investigating use of wearable sensors [16, 87]. Without clear guidelines, measurement error can be introduced and/or exacerbated depending on the procedures followed.

The methods of calibration also varied between studies, with a static anatomical calibration the most commonly utilised method (typically adopting a neutral pose, standing with arms by the side and palms facing forward, as recommended by most manufacturers). Dynamic anatomical calibration was often customised to suit the needs of the study and the joint being measured. For example, dynamic anatomical calibration of the elbow varied from repetitions of flexion and extension at various speeds [59], to the rapid movement of the arm from 45° to neutral [42]. Details of the dynamic anatomical calibrations were omitted in some studies, limiting replication. More pertinent for the calculation of joint kinematics is anatomical calibration as compared to system calibration, with the type of calibration (i.e. static or dynamic) and movements of the dynamic anatomical calibration, having a significant impact on the accuracy of wearable sensors [69].

Of the 66 studies included in this review, almost half (45%) were validation studies with the remaining studies using wearable sensors as an outcome measure. Over one third (29%) were conference proceedings in the field of engineering, thus limiting the amount of information available. The median sample size was 7.6 participants per study; only one study was considered to have an adequate sample size for the validation of a measurement tool as per the COSMIN guidelines [19]. The majority (78%) of the results were obtained from healthy adults, with clinical populations (12%) and those under the age of 18 (1.5%) not well represented. Research investigating the use of wearable sensors to measure lower limb kinematics has demonstrated a level of accuracy with clinical populations and children. Errors < 4° were reported for elderly individuals with hemiparesis [88] and RMSEs between 4.6 and 8.8° for children with spastic cerebral palsy [10]. There is potential for wearable sensors to be applied to the upper limb of these populations; however, more research is required to determine the optimal procedures prior to implementation in clinical practice.

The validity and reliability of wearable sensors when applied to the upper limb has not been clearly described to date. When compared to a robotic device, the commercial wearable sensors with customised software recorded errors below McGinley’s [7] suggested 5.0° threshold. Less than 3.9° was reported for replica/simulated movements of the wrist in 3DOF [28, 46, 48, 56], < 3.1° for 2DOF at the elbow [46, 48, 56], and < 2.5° for 1DOF (flexion/extension) at the shoulder [48]. Shoulder internal and external rotation resulted in the largest error (3.0–9.7°) [48], and therefore, results for this movement should be interpreted with caution.

The next section will discuss ‘in vivo’ studies with 3DMA as a pseudo gold standard. Studies that made a direct comparison between the wearable sensors and 3DMA system (i.e. used the same segment tracking) demonstrated differences that exceeded the suggested 5.0° threshold, with up to 15.0° difference reported for the elbow. However, depending on the software specifications and level of customisation, a difference of < 0.11° (3DOF shoulder), < 0.41° (2DOF elbow), and < 2.6 (2DOF wrist) was achievable. The range in difference observed between the two systems is indicative that wearable sensors are still largely in a ‘developmental phase’ for the measurement of joint angle in the upper limb.

Consistent with prior findings, error values were unique to the joint and movement tasks being measured. Most of the tasks involved movements in multiple planes (i.e. reaching tasks), which resulted in more error compared to studies that assessed isolated movement in a single plane (i.e. flexion and extension). Measuring multiple planes of movement poses a further challenge to motion analysis and needs careful consideration when interpreting the results [89].

Limitations

Due to the heterogeneity in the reported studies, a meta-analysis was not appropriate given the variance in sample sizes, movement tasks, different procedures, and statistical analyses used. It was also not possible to apply a standard assessment of quality and bias due to the diversity of the studies. The inclusion of small samples (30% single participant) is a potential threat to validity, with single participant analysis insufficient to support robustness and generalisation of the evidence. The inclusion of conference papers (28%) meant that many papers provided limited detail on the proposed system and validation results. Small sample sizes and the inclusion of mostly healthy adults means the results of this review cannot be generalised to wider clinical populations. In addition, studies that utilised different segment tracking (i.e. 3DMA markers were not mounted on the wearable sensor) were not further analysed as it was not possible to delineate between the sources of error.

Conclusion

Wearable sensors have become smaller, more user-friendly, and increasingly accurate. The evidence presented suggests that wearable sensors have great potential to bridge the gap between laboratory-based systems and the goniometer for the measurement of upper limb joint angle during dynamic movement. A level of acceptable accuracy was demonstrated for the measurement of elbow and wrist flexion/extension when compared to a robotic device. Error was influenced by the fusion algorithm and method of joint calculation, which required customisation to achieve errors < 2.9° from known angles on a robotic device. Higher error margins were observed in vivo when compared to a 3DMA system, but < 5° was achievable with a high level of customisation. The additional level of customisation that was often required to achieve results with minimal error is particularly relevant to clinicians with limited technical support, and critically, when using a system ‘off the shelf’, the expected level of accuracy may not be comparable to the findings reported in this review.

With this technology rapidly evolving, future research should establish standardised protocol/guidelines, and subsequent reliability and validity for use in the upper limb, and in various clinical populations. Direct comparisons with the gold standard (i.e. same segment tracking) is needed to produce results that are most meaningful. We recommend and encourage the use of wearable sensors for the measurement of flexion/extension in the wrist and elbow; however, this should be combined with outcome measures that have demonstrated reliability and validity in the intended population.

Abbreviations

3DMA: 

Three-dimensional motion analysis

Ab: 

Abduction

Acc: 

Acceleration

Ad: 

Adduction

C: 

Customised

CMC: 

Coefficient of multiple correlations

Con: 

Conference paper

Dep: 

Depression

DOF: 

Degrees of freedom

DS: 

Double sided

EKF: 

Extended Kalman filter

Elev: 

Elevation

Ext rot: 

External rotation

Ext: 

Extension

Flex: 

Flexion

Full: 

Full text

Gyr: 

Gyroscope

ICC: 

Intra-class correlation coefficient

Int rot: 

Internal rotation

KBF: 

Kalman-based filter

KF: 

Kalman filter

M: 

Manufacturer

Mag: 

Magnetometer

PD: 

Parkinson’s disease

Pro: 

Pronation

Rad dev: 

Radial deviation

RMSE: 

Root mean square error

ROM: 

Range of motion

SCI: 

Spinal cord injury

SD: 

Standard deviation

Sup: 

Supination

UKF: 

Unscented Kalman filter

Uln dev: 

Ulnar deviation

Declarations

Acknowledgements

This research was completed with financial support from the Australian Government Research Training Program Scholarship and the Perth Children’s Hospital Foundation. The authors would like to acknowledge the support of Curtin University: the School of Occupational Therapy, Social Work and Speech Pathology, the School of Physiotherapy and Sport Science, and the Faculty of Health Science’s librarian, Diana Blackwood. Further acknowledgement is extended to the Australian Catholic University and Centre for Research Excellence in Cerebral Palsy.

Funding

The authors wish to thank the School of Occupational Therapy, Social Work and Speech Pathology at Curtin University who provided funding.

Availability of Data and Materials

Data presented in this systematic review is available in the associated studies, and references are provided.

Authors’ Contributions

All authors read and approved the final manuscript.

Authors’ information

Not applicable

Ethics Approval and Consent to Participate

Ethical approval was not required for this systematic review.

Consent for Publication

Not applicable as this manuscript does not include any individual person’s data.

Competing Interests

The authors Corrin Walmsley, Sian Williams, Tiffany Grisbrook, Catherine Elliott, Christine Imms, and Amity Campbell declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, 6027, Australia
(2)
School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, 6027, Australia
(3)
Department of Surgery, University of Auckland, Auckland, 1010, New Zealand
(4)
Kids Rehab WA, Perth Children’s Hospital, Perth, WA, 6008, Australia
(5)
Centre for Disability and Development Research, School of Allied Health, Australian Catholic University, Melbourne, VIC, 3065, Australia

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