Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Search Strategy
2.2. Study Selection Process
2.3. Data Extraction Process
3. Results
4. Discussion
4.1. Instrumented Objects and Glove-Based Systems
4.2. Body-Networked Sensor and Vision-Based Motion Capture Systems
4.3. End-Effector and Exoskeleton Systems
4.4. Impact of Quantitative Measurements on Clinical Practice
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References | Evaluated Task | Metrics |
---|---|---|
[22] | ADLs (real) | Amount of hand use |
[23] | Finger flexion/extension | Joint ROM, performance score, error rate (%) |
[24] | ARAT test | Joint ROM, velocity, pinch force |
[25] | ADLs (real) | Activity detection |
[26] | Grasp task | Movement smoothness, movement efficiency, movement speed, smoothness of grip force |
[27] | Finger pinch | Force reaction time (ms), peak force (N), maximum rate of force change (N/s), end-point accuracy, variability metrics (mean, SD, percentage on target) |
[28] | Drinking task: filling, grasping, manipulating and releasing. | Mean force, orientation (degrees), velocity, tremor detection (translational and rotational), liquid level |
[29] | ADLs (real) | Amount of hand use |
[30] | Reaching, grasp, releasing | Grasp detection |
[31] | Finger flexion/extension, grasp task | Joint ROM, mean velocity, peak velocity |
[32] | Finger opposition movements, finger flexion/extension | Joint ROM, velocity, acceleration |
[33] | Tripod grasp | Force (N), time |
[34] | SHUEE test | Reaching ROM, velocity, acceleration |
[35] | Manipulation, reaching, grasping tasks | Joint ROM, velocity, movement accuracy |
[36] | Finger flexion/extension | Joint ROM, velocity, probability of success task, peak force |
[37] | Finger opposition movements | Touch duration (ms), inter tapping interval (ms), movement rate (Hz), inter hand interval (ms) |
[38] | Reaching movements | Reaching ROM |
[39] | Hand grip, finger pinch | Hand grip strength (N), pinch force (N) |
[40] | Hand grip task | Pressure: mean absolute difference, mean absolute variance from target |
[41] | Prehensile task, manipulation task | Joint ROM, trajectories |
[42] | Grasp, release task | Range of grip pressure |
[43] | Grasp task | Grasping force, amount of hand use |
[44] | Finger flexion/extension | Joint ROM |
References
- Scott, D. Functional Hand Evaluations: A Review. Am. J. Occup. Ther. Off. Publ. Am. Occup. Ther. Assoc. 1987, 535, 158–163. [Google Scholar]
- Trybus, M.; Lorkowski, J.; Brongel, L.; Hladki, W. Causes and Consequences of Hand Injuries. Am. J. Surg. 2006, 192, 52–57. [Google Scholar] [CrossRef]
- Cooper, C. Fundamentals. Fundamentals of Hand Therapy; Elsevier: Amsterdam, The Netherlands, 2014; pp. 1–14. [Google Scholar]
- Hawe, R.L.; Scott, S.H.; Dukelow, S.P. Taking Proportional out of Stroke Recovery. Stroke 2018, 50, STROKEAHA118023006. [Google Scholar] [CrossRef]
- Hope, T.M.H.; Friston, K.; Price, C.J.; Leff, A.P.; Rotshtein, P.; Bowman, H. Recovery after Stroke: Not so Proportional after All? Brain 2019, 142, 15–22. [Google Scholar] [CrossRef]
- World Health Organization. International Classification of Functioning, Disability and Health; World Health Organization: Genève, Switzerland, 2009. [Google Scholar]
- Alt Murphy, M.; Häger, C.K. Kinematic Analysis of the Upper Extremity after Stroke—How Far Have We Reached and What Have We Grasped? Phys. Ther. Rev. 2015, 20, 137–155. [Google Scholar] [CrossRef]
- Schwarz, A.; Kanzler, C.M.; Lambercy, O.; Luft, A.R.; Veerbeek, J.M. Systematic Review on Kinematic Assessments of Upper Limb Movements after Stroke. Stroke 2019, 50, 718–727. [Google Scholar] [CrossRef]
- Noorkõiv, M.; Rodgers, H.; Price, C.I. Accelerometer Measurement of Upper Extremity Movement after Stroke: A Systematic Review of Clinical Studies. J. Neuroeng. Rehabil. 2014, 11, 144. [Google Scholar] [CrossRef] [Green Version]
- de los Reyes-Guzmán, A.; Dimbwadyo-Terrer, I.; Trincado-Alonso, F.; Monasterio-Huelin, F.; Torricelli, D.; Gil-Agudo, A. Quantitative Assessment Based on Kinematic Measures of Functional Impairments during Upper Extremity Movements: A Review. Clin. Biomech. 2014, 29, 719–727. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ellis, M.D.; Lan, Y.; Yao, J.; Dewald, J.P.A. Robotic Quantification of Upper Extremity Loss of Independent Joint Control or Flexion Synergy in Individuals with Hemiparetic Stroke: A Review of Paradigms Addressing the Effects of Shoulder Abduction Loading. J. Neuroeng. Rehabil. 2016, 13, 95. [Google Scholar] [CrossRef] [Green Version]
- Nowak, D.A.; Hermsdörfer, J. Objective Evaluation of Manual Performance Deficits in Neurological Movement Disorders. Brain Res. Rev. 2006, 51, 108–124. [Google Scholar] [CrossRef]
- Henderson, J.; Condell, J.; Connolly, J.; Kelly, D.; Curran, K. Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis. Sensors 2021, 21, 1576. [Google Scholar] [CrossRef] [PubMed]
- Cochrane Handbook for Systematic Reviews of Interventions; Higgins, J.; Green, S. (Eds.) John Wiley & Sons Ltd.: Chichester, UK, 2011. [Google Scholar]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- U.S. Department of Energy. Technology Readiness, Assessment Guide, Office of Management; U.S. Department of Energy: Washington, DC, USA, 2009.
- Dipietro, L.; Sabatini, A.M.; Dario, P. A Survey of Glove-Based Systems and Their Applications. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 2008, 38, 461–482. [Google Scholar] [CrossRef]
- Poon, C.C.Y.; Lo, B.P.L.; Yuce, M.R.; Alomainy, A.; Hao, Y. Body Sensor Networks: In the Era of Big Data and Beyond. IEEE Rev. Biomed. Eng. 2015, 8, 4–16. [Google Scholar] [CrossRef] [PubMed]
- Moeslund, T.B.; Granum, E. A Survey of Computer Vision-Based Human Motion Capture. Comput. Vis. Image Underst. 2001, 81, 231–268. [Google Scholar] [CrossRef]
- Molteni, F.; Gasperini, G.; Cannaviello, G.; Guanziroli, E. Exoskeleton and End-Effector Robots for Upper and Lower Limbs Rehabilitation: Narrative Review. PM R 2018, 10, S174–S188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ven-Stevens, L.; Graff, M.; Selles, R.; Schreuders, T.; Linde, H.; Spauwen, P.; Geurts, A. Instruments for Assessment of Impairments and Activity Limitations in Patients with Hand Conditions: A European Delphi Study. J. Rehabil. Med. 2015, 47, 948–956. [Google Scholar] [CrossRef] [Green Version]
- Schwerz de Lucena, D.; Rowe, J.; Chan, V.; Reinkensmeyer, D. Magnetically Counting Hand Movements: Validation of a Calibration-Free Algorithm and Application to Testing the Threshold Hypothesis of Real-World Hand Use after Stroke. Sensors 2021, 21, 1502. [Google Scholar] [CrossRef]
- Jha, C.K.; Gajapure, K.; Chakraborty, A.L. Design and Evaluation of an FBG Sensor-Based Glove to Simultaneously Monitor Flexure of Ten Finger Joints. IEEE Sens. J. 2021, 21, 7620–7630. [Google Scholar] [CrossRef]
- Schwarz, A.; Bhagubai, M.M.C.; Wolterink, G.; Held, J.P.O.; Luft, A.R.; Veltink, P.H. Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing. Sensors 2020, 20, 4770. [Google Scholar] [CrossRef]
- Visee, R.J.; Likitlersuang, J.; Zariffa, J. An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 748–755. [Google Scholar] [CrossRef]
- Kanzler, C.M.; Schwarz, A.; Held, J.P.O.; Luft, A.R.; Gassert, R.; Lambercy, O. Technology-Aided Assessment of Functionally Relevant Sensorimotor Impairments in Arm and Hand of Post-Stroke Individuals. J. Neuroeng. Rehabil. 2020, 17, 1–15. [Google Scholar] [CrossRef]
- Barlow, S.; Custead, R.; Lee, J.; Hozan, M.; Greenwood, J. Wireless Sensing of Lower Lip and Thumb-Index Finger ‘Ramp-and-Hold’ Isometric Force Dynamics in a Small Cohort of Unilateral MCA Stroke: Discussion of Preliminary Findings. Sensors 2020, 20, 1221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bobin, M.; Anastassova, M.; Boukallel, M.; Ammi, M. Design and Study of a Smart Cup for Monitoring the Arm and Hand Activity of Stroke Patients. IEEE J. Transl. Eng. Health Med. 2018, 6, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Rajan, S.; Ramasarma, N.; Bonato, P.; Lee, S.I. The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings. IEEE J. Biomed. Health Inform. 2019, 23, 599–606. [Google Scholar] [CrossRef] [PubMed]
- Sadarangani, G.P.; Jiang, X.; Simpson, L.A.; Eng, J.J.; Menon, C. Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment. Front. Bioeng. Biotechnol. 2017, 5, 42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schreck, M.J.; Kelly, M.; Lander, S.; Kaushik, A.; Smith, H.; Bell, S.; Raman, V.; Olles, D.; Geigel, J.; Olles, M.; et al. Dynamic Functional Assessment of Hand Motion Using an Animation Glove: The Effect of Stenosing Tenosynovitis. Hand 2018, 13, 695–704. [Google Scholar] [CrossRef]
- Spasojević, S.; Ilić, T.V.; Milanović, S.; Potkonjak, V.; Rodić, A.; Santos-Victor, J. Combined Vision and Wearable Sensors-Based System for Movement Analysis in Rehabilitation. Methods Inf. Med. 2017, 56, 95–111. [Google Scholar] [CrossRef] [Green Version]
- Romeo, R.A.; Cordella, F.; Zollo, L.; Formica, D.; Saccomandi, P.; Schena, E.; Carpino, G.; Davalli, A.; Sacchetti, R.; Guglielmelli, E. Development and Preliminary Testing of an Instrumented Object for Force Analysis during Grasping. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015. [Google Scholar]
- Rammer, J.R.; Krzak, J.J.; Riedel, S.A.; Harris, G.F. Evaluation of Upper Extremity Movement Characteristics during Standardized Pediatric Functional Assessment with a Kinect®-Based Markerless Motion Analysis System. In Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 27–31 August 2014. [Google Scholar]
- Schuster-Amft, C.; Eng, K.; Lehmann, I.; Schmid, L.; Kobashi, N.; Thaler, I.; Verra, M.L.; Henneke, A.; Signer, S.; McCaskey, M.; et al. Using Mixed Methods to Evaluate Efficacy and User Expectations of a Virtual Reality–Based Training System for Upper-Limb Recovery in Patients after Stroke: A Study Protocol for a Randomised Controlled Trial. Trials 2014, 15, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Taheri, H.; Rowe, J.B.; Gardner, D.; Chan, V.; Gray, K.; Bower, C.; Reinkensmeyer, D.J.; Wolbrecht, E.T. Design and Preliminary Evaluation of the FINGER Rehabilitation Robot: Controlling Challenge and Quantifying Finger Individuation during Musical Computer Game Play. J. Neuroeng. Rehabil. 2014, 11, 10. [Google Scholar] [CrossRef] [Green Version]
- Bonzano, L.; Sormani, M.P.; Tacchino, A.; Abate, L.; Lapucci, C.; Mancardi, G.L.; Uccelli, A.; Bove, M. Quantitative Assessment of Finger Motor Impairment in Multiple Sclerosis. PLoS ONE 2013, 8, e65225. [Google Scholar] [CrossRef] [Green Version]
- Kurillo, G.; Han, J.J.; Obdržálek, S.; Yan, P.; Abresch, R.T.; Nicorici, A.; Bajcsy, R. Upper Extremity Reachable Workspace Evaluation with Kinect. Stud. Health Technol. Inform. 2013, 184, 247–253. [Google Scholar] [PubMed]
- Nica, A.S.; Brailescu, C.M.; Scarlet, R.G. Virtual Reality as a Method for Evaluation and Therapy after Trau-Matic Hand Surgery. Stud. Health Technol. Inform. 2013, 191, 48–52. [Google Scholar] [PubMed]
- Lee, S.I.; Ghasemzadeh, H.; Mortazavi, B.J.; Sarrafzadeh, M. A Pervasive Assessment of Motor Function: A Lightweight Grip Strength Tracking System. IEEE J. Biomed. Health Inform. 2013, 17, 1023–1030. [Google Scholar] [CrossRef]
- Oess, N.P.; Wanek, J.; Curt, A. Design and Evaluation of a Low-Cost Instrumented Glove for Hand Function Assessment. J. Neuroeng. Rehabil. 2012, 9, 2. [Google Scholar] [CrossRef] [Green Version]
- Zariffa, J.; Kapadia, N.; Kramer, J.L.K.; Taylor, P.; Alizadeh-Meghrazi, M.; Zivanovic, V.; Albisser, U.; Willms, R.; Townson, A.; Curt, A.; et al. Relationship between Clinical Assessments of Function and Measurements from an Upper-Limb Robotic Rehabilitation Device in Cervical Spinal Cord Injury. IEEE Trans. Neural Syst. Rehabil. Eng. 2012, 20, 341–350. [Google Scholar] [CrossRef] [PubMed]
- Sgandurra, G.; Cecchi, F.; Serio, S.M.; Del Maestro, M.; Laschi, C.; Dario, P.; Cioni, G. Longitudinal Study of Unimanual Actions and Grasping Forces during Infancy. Infant Behav. Dev. 2012, 35, 205–214. [Google Scholar] [CrossRef] [PubMed]
- Golomb, M.R.; McDonald, B.C.; Warden, S.J.; Yonkman, J.; Saykin, A.J.; Shirley, B.; Huber, M.; Rabin, B.; AbdelBaky, M.; Nwosu, M.E.; et al. In-Home Virtual Reality Videogame Telerehabilitation in Adolescents with Hemiplegic Cerebral Palsy. Arch. Phys. Med. Rehabil. 2010, 91, 1–8.e1. [Google Scholar] [CrossRef]
- Park, J.W.; Kim, T.; Kim, D.; Hong, Y.; Gong, H.S. Measurement of Finger Joint Angle Using Stretchable Carbon Nano-Tube Strain Sensor. PLoS ONE 2019, 14, e0225164. [Google Scholar] [CrossRef]
- Zhu, M.; Sun, Z.; Chen, T.; Lee, C. Low Cost Exoskeleton Manipulator Using Bidirectional Triboelectric Sensors Enhanced Multiple Degree of Freedom Sensory System. Nat. Commun. 2021, 12, 2692. [Google Scholar] [CrossRef]
- Wang, L.; Meydan, T.; Williams, P. Design and Evaluation of a 3-D Printed Optical Sensor for Monitoring Finger Flexion. IEEE Sens. J. 2017, 17, 1937–1944. [Google Scholar] [CrossRef] [Green Version]
- Da Silva, A.F.; Goncalves, A.F.; Mendes, P.M.; Correia, J.H. FBG Sensing Glove for Monitoring Hand Posture. IEEE Sens. J. 2011, 11, 2442–2448. [Google Scholar] [CrossRef]
- Wittmann, F.; Lambercy, O.; Gonzenbach, R.R.; van Raai, M.A.; Hover, R.; Held, J.; Starkey, M.L.; Curt, A.; Luft, A.; Gassert, R. Assessment-Driven Arm Therapy at Home Using an IMU-Based Virtual Reality System. In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, 11–14 August 2015. [Google Scholar]
- Balasubramanian, S.; Klein, J.; Burdet, E. Robot-Assisted Rehabilitation of Hand Function. Curr. Opin. Neurol. 2010, 23, 661–670. [Google Scholar] [CrossRef] [PubMed]
- Kang, Y.; Ding, H.; Zhou, H.; Wei, Z.; Liu, L.; Pan, D.; Feng, S. Epidemiology of Worldwide Spinal Cord Injury: A Literature Review. J. Neurorestoratol. 2017, 6, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Porter, G.; Taggart, L. The Neurological Hand. In Fundamentals of Hand Therapy; Elsevier: Amsterdam, The Netherlands, 2014; pp. 551–566. [Google Scholar]
- Dorsey, E.R.; Papapetropoulos, S.; Xiong, M.; Kieburtz, K. The First Frontier: Digital Biomarkers for Neurodegenerative Disorders. Digit. Biomark. 2017. [Google Scholar] [CrossRef]
- Sreenivasa, M.; Valero-Cuevas, F.J.; Tresch, M.; Nakamura, Y.; Schouten, A.C.; Sartori, M. Editorial: Neuromechanics and Control of Physical Behavior: From Experimental and Computational Formulations to Bio-Inspired Technologies. Front. Comput. Neurosci. 2019, 13, 13. [Google Scholar] [CrossRef] [PubMed]
- Serio, S.M.; Cecchi, F.; Boldrini, E.; Laschi, C.; Sgandurra, G.; Cioni, G.; Dario, P. Instrumented Toys for Studying Power and Precision Grasp Forces in Infants. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow, UK, 11–15 July 2021. [Google Scholar]
- Roalf, D.R.; Rupert, P.; Mechanic-Hamilton, D.; Brennan, L.; Duda, J.E.; Weintraub, D.; Trojanowski, J.Q.; Wolk, D.; Moberg, P.J. Quantitative Assessment of Finger Tapping Characteristics in Mild Cognitive Impairment, Alzheimer’s Disease, and Parkinson’s Disease. J. Neurol. 2018, 265, 1365–1375. [Google Scholar] [CrossRef]
- Jeppesen Kragh, F.; Bruun, M.; Budtz-Jørgensen, E.; Hjermind, L.E.; Schubert, R.; Reilmann, R.; Nielsen, J.E.; Hasselbalch, S.G. Quantitative Measurements of Motor Function in Alzheimer’s Disease, Frontotemporal Dementia, and Dementia with Lewy Bodies: A Proof-of-Concept Study. Dement. Geriatr. Cogn. Disord. 2018, 46, 168–179. [Google Scholar] [CrossRef]
- Haberfehlner, H.; Goudriaan, M.; Bonouvrié, L.A.; Jansma, E.P.; Harlaar, J.; Vermeulen, R.J.; van der Krogt, M.M.; Buizer, A.I. Instrumented Assessment of Motor Function in Dyskinetic Cerebral Palsy: A Systematic Review. J. Neuroeng. Rehabil. 2020, 17, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Hotham, E.; Haberfield, M.; Hillier, S.; White, J.M.; Todd, G. Upper Limb Function in Children with Attention-Deficit/Hyperactivity Disorder (ADHD). J. Neural Transm. 2018, 125, 713–726. [Google Scholar] [CrossRef]
- Li-Tsang, C.W.P.; Li, T.M.H.; Ho, C.H.Y.; Lau, M.S.W.; Leung, H.W.H. The Relationship between Sensorimotor and Handwriting Performance in Chinese Adolescents with Autism Spectrum Disorder. J. Autism Dev. Disord. 2018, 48, 3093–3100. [Google Scholar] [CrossRef] [PubMed]
Concept | Search Terms |
---|---|
Assessment | functional assessment OR monitoring |
AND | |
Functions/Impairment | range of motion OR muscle power OR fine hand use OR hand activity OR fine impairment |
AND | |
Upper extremity | upper extremity OR hand OR finger |
AND | |
Technology-aided approach | technology OR quantitative OR robot OR sensors OR sensor system OR wearable systems OR mobile OR kinematic OR kinetic NOT electromyography |
First Author, Year | Ref. | Sensing Technology | System | Communication Protocols | Calibration | Feedback | Data | Evaluation Type | Activity | Target Functions | Target Population | Setting | TRL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Schwerz de Lucena, 2021 | [22] | Magnetometers, IMU | Body- networked sensor system (Wristband and ring) | Wireless | - | Visual | Kinematic | Performance | ADL | Fine hand use; hand and arm use | Chronic stroke (n = 29) | Home | TRL 6 |
Jha, 2021 | [23] | Fiber optical sensors | Glove-based system | Wired | Required | VR | Kinematic | Capacity | Basic task | Mobility of joint functions; Fine hand use | Healthy subjects (n = 5) | Lab | TRL 4 |
Schwar, 2020 | [24] | Force sensor, IMUs | Body- networked sensor system | Wired | Required | - | Kinematic kinetic | Capacity | Functional task | Mobility of joint functions; Muscle power function; Fine hand use; Hand and arm use | Chronic stroke (n = 10) | Clin | TRL 5 |
Visée, 2020 | [25] | GoPro camera sensor | Vision-based motion capture system | Wireless | - | - | Kinematic | Performance | ADL | Hand and arm use | Spinal cord injury (n = 17) | Lab | TRL 4 |
Kanzler, 2020 | [26] | Force sensor | End-effector | Wired | - | VR haptic | Kinematic kinetic | Capacity | Basic task | Fine hand use; hand and arm use | Stroke (n = 30) | Clin | TRL 7 |
Barlow, 2020 | [27] | Strain gage sensors (bulit-in load cell) | Instrumented object | Wireless | Required | Visual acoustic | Kinetic | Capacity | Basic task | Muscle power functions; Fine hand use | Chronic stroke (n = 7); Healthy subjects (n = 25) | Lab | TRL 4 |
Bobin, 2018 | [28] | Pressure sensors (FSR), conductive electrodes, IMU | Instrumented object (Smart cup) | Wireless | Required | Visual acoustic | Kinematic kinetic | Capacity Performance | Functional task; ADL | Muscle power functions; Fine hand use; Hand and arm use | Stroke (n = 9) | Clin Home | TRL 7 |
Liu, 2019 | [29] | IMUs | Body-networked sensor system (finger worn sensor, wrist worn sensor) | Wireless | - | - | Kinematic | Performance | ADL | Hand and arm use | Healthy subjects (n = 18) | Lab | TRL 4 |
Sadarangani, 2017 | [30] | Force sensors (FSR) | Body-networked sensor system (Smartband) | Wired | Required | - | Kinetic | Performance | Functional task | Hand and arm use | Stroke (n = 8); Healthy subjects (n = 8) | Lab | TRL 4 |
Schreck, 2017 | [31] | Resistive bend sensors | Glove-based system | Wireless | Required | Visual | Kinematic | Capacity | Basic task | Mobility of joint functions; Fine hand use | Healthy subjects (n = 10); Stenosing tenosynovitis (n = 11) | Clin | TRL 9 |
Spasojević, 2017 | [32] | Resistive bend sensors | Glove-based system | Wireless | Required | - | Kinematic | Capacity | Basic task | Mobility of joint functions; Fine hand use | Parkinson’s disease (n = 30); Healthy subjects (n = 23) | Clin | TRL 9 |
Romeo, 2015 | [33] | Force sensor (FSR) | Instrumented object | Wired | Required | - | Kinetic | Capacity | Basic task | Muscle power functions; Fine hand use | Healthy subject (n = 1) | Lab | TRL 3 |
Rammer, 2014 | [34] | Microsoft Kinect sensor | Vision-based motion capture system | Wireless | - | - | Kinematic | Capacity | Functional task | Mobility of joint functions; Fine hand use; Hand and arm use | Healthy adolescent subjects (n = 12) | Clin | TRL 9 |
Schuster-Amft, 2014 | [35] | Resistive bend sensors | Instrumented object (smart cup) | Wireless | - | VR | Kinematic | Capacity | Functional task | Mobility of joint functions; Fine hand use; Hand and arm use | Chronic stroke (n = 60) | Clin | TRL 9 |
Taheri, 2014 | [36] | Hall Effect sensors | Exoskeleton | - | - | VR | Kinematic kinetic | Capacity | Basic task | Mobility of joint functions; Muscle power functions; Fine hand use | Stroke (n = 16) | Lab | TRL 4 |
Bonzano, 2013 | [37] | Electrical con- tacts | Glove-based system | Wired | - | Visual acoustic | Kinematic | Capacity | Basic task | Fine hand use | Multiple sclerosis (n = 40) | Clin | TRL 8 |
Kurillo, 2013 | [38] | Microsoft Kinect sensor | Vision-based motion capture system | Wireless | Required | Visual | Kinematic | Capacity | Functional task | Mobility of joint functions; Hand and arm use | Healthy subjects (n = 10) | Lab | TRL 9 |
Nica, 2013 | [39] | Force sensor | Instrumented object | Wired | - | VR | Kinetic | Capacity | Basic task | Musclepower functions; Fine hand use | Hand traumatic injuries (n = 54) | Clin | TRL 9 |
Lee, 2013 | [40] | Force sensor (FSR) | Instrumented object | Wireless | Required | Visual | Kinetic | Capacity | Basic task | Muscle power functions; Fine hand use | Stroke and CIDP (n = 12); Healthy subjects (n = 4) | Clin | TRL 5 |
Oess, 2012 | [41] | Resistive bend sensors | Glove-based system | Wired | - | - | Kinematic | Capacity | Functional task | Mobility of joint functions; Fine hand use; Hand and arm use | Healthy subjects (n = 10); Cervical spine cord injury (n = 4) | Clin | TRL 5 |
Zariffa, 2012 | [42] | Pressure sensor | Exoskeleton | Wired | Required | VR | Kinetic | Capacity | Basic task | Muscle power functions; Hand and arm use | Spinal cord injury (n = 14) | Clin | TRL 9 |
Sgandurra, 2012 | [43] | Piezoresistive pressure sensor | Instrumented object (ring- shaped toy) | - | - | - | Kinetic | Capacity | Basic task | Muscle power functions; Fine hand use; Hand and arm use | Developing infants from 4-9 months (n = 10) | Home | TRL 9 |
Golomb, 2010 | [44] | Fiber optical sensors | Glove-based system | Wired | Required | VR (game) | Kinematic | Capacity | Basic task | Mobility of joint functions; Finehand use | Adolescent with cerebral palsy (n = 3) | Home | TRL 7 |
Category | Target Population | References |
---|---|---|
Neurological disease | Stroke | [22,24,26,27,28,30,35,36,40] |
Spinal cord injury | [25,41,42] | |
Parkinson’s disease | [32] | |
Multiple sclerosis | [37] | |
CIDP | [40] | |
Cerebral palsy | [44] | |
Musculoskeletal impairment | Stenosing tenosynovitis | [31] |
Traumatic injuries | [39] | |
Others | Healthy subjects | [23,27,29,30,31,32,33,34,38,40,41] |
Developing infants | [43] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mennella, C.; Alloisio, S.; Novellino, A.; Viti, F. Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review. Sensors 2022, 22, 199. https://doi.org/10.3390/s22010199
Mennella C, Alloisio S, Novellino A, Viti F. Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review. Sensors. 2022; 22(1):199. https://doi.org/10.3390/s22010199
Chicago/Turabian StyleMennella, Ciro, Susanna Alloisio, Antonio Novellino, and Federica Viti. 2022. "Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review" Sensors 22, no. 1: 199. https://doi.org/10.3390/s22010199
APA StyleMennella, C., Alloisio, S., Novellino, A., & Viti, F. (2022). Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review. Sensors, 22(1), 199. https://doi.org/10.3390/s22010199