1
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Wang B, Hargrove L, Bao X, Kamavuako EN. Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings. SENSORS (BASEL, SWITZERLAND) 2022; 22:9849. [PMID: 36560218 PMCID: PMC9786766 DOI: 10.3390/s22249849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance.
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Affiliation(s)
- Bingbin Wang
- Department of Engineering, King’s College London, London WC2R 2LS, UK
| | - Levi Hargrove
- Center for Bionic Medicine, the Shirley Ryan Ability, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xinqi Bao
- Department of Engineering, King’s College London, London WC2R 2LS, UK
| | - Ernest N. Kamavuako
- Department of Engineering, King’s College London, London WC2R 2LS, UK
- Faculté de Médecine, Université de Kindu, Site de Lwama II, Kindu, Maniema, Congo
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2
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Nanivadekar AC, Chandrasekaran S, Helm ER, Boninger ML, Collinger JL, Gaunt RA, Fisher LE. Closed-loop stimulation of lateral cervical spinal cord in upper-limb amputees to enable sensory discrimination: a case study. Sci Rep 2022; 12:17002. [PMID: 36220864 PMCID: PMC9553970 DOI: 10.1038/s41598-022-21264-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/26/2022] [Indexed: 12/29/2022] Open
Abstract
Modern myoelectric prosthetic hands have multiple independently controllable degrees of freedom, but require constant visual attention to use effectively. Somatosensory feedback provides information not available through vision alone and is essential for fine motor control of our limbs. Similarly, stimulation of the nervous system can potentially provide artificial somatosensory feedback to reduce the reliance on visual cues to efficiently operate prosthetic devices. We have shown previously that epidural stimulation of the lateral cervical spinal cord can evoke tactile sensations perceived as emanating from the missing arm and hand in people with upper-limb amputation. In this case study, two subjects with upper-limb amputation used this somatotopically-matched tactile feedback to discriminate object size and compliance while controlling a prosthetic hand. With less than 30 min of practice each day, both subjects were able to use artificial somatosensory feedback to perform a subset of the discrimination tasks at a success level well above chance. Subject 1 was consistently more adept at determining object size (74% accuracy; chance: 33%) while Subject 2 achieved a higher accuracy level in determining object compliance (60% accuracy; chance 33%). In each subject, discrimination of the other object property was only slightly above or at chance level suggesting that the task design and stimulation encoding scheme are important determinants of which object property could be reliably identified. Our observations suggest that changes in the intensity of artificial somatosensory feedback provided via spinal cord stimulation can be readily used to infer information about object properties with minimal training.
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Affiliation(s)
- Ameya C. Nanivadekar
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.509981.c0000 0004 7644 8442Center for Neural Basis of Cognition, Pittsburgh, PA 15213 USA
| | - Santosh Chandrasekaran
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.509981.c0000 0004 7644 8442Center for Neural Basis of Cognition, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Eric R. Helm
- grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Michael L. Boninger
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000University of Pittsburgh Clinical Translational Science Institute, Pittsburgh, PA 15213 USA
| | - Jennifer L. Collinger
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.509981.c0000 0004 7644 8442Center for Neural Basis of Cognition, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA ,Human Engineering Research Labs, Department of Veteran Affairs, VA Center of Excellence, Pittsburgh, PA 15206 USA ,grid.147455.60000 0001 2097 0344Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA USA
| | - Robert A. Gaunt
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.509981.c0000 0004 7644 8442Center for Neural Basis of Cognition, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.147455.60000 0001 2097 0344Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA USA
| | - Lee E. Fisher
- grid.21925.3d0000 0004 1936 9000Rehab Neural Engineering Labs, University of Pittsburgh, 3520 Fifth Avenue, Suite 300, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.509981.c0000 0004 7644 8442Center for Neural Basis of Cognition, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.147455.60000 0001 2097 0344Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA USA
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3
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Salminger S, Gstoettner C, Sturma A, Mayer JA, Papst H, Aszmann OC. Actual prosthetic usage in relation to functional outcomes and wearing time in individuals with below-elbow amputation. Prosthet Orthot Int 2022; 46:408-413. [PMID: 35511449 DOI: 10.1097/pxr.0000000000000137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/14/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Wearing time of a prosthesis is regarded as an indicator for success of prosthetic rehabilitation. However, prostheses are frequently worn for esthetic purposes only. Although different supervised measurements to assess prosthetic dexterity are used, it is not clear how performance in such tests translates into actual use in everyday life. OBJECTIVES To evaluate the actual daily use of the prosthetic device in patients with below-elbow amputations by recording the number of grasping motions. STUDY DESIGN Observational study. METHODS Upper extremity function was evaluated using different objective and timed assessments in five unilateral patients with below-elbow amputations. In addition, patients reported daily wearing time, and the number of performed prosthetic movements over a period of at least three months was recorded. RESULTS The patients achieved a mean Southampton Hand Assessment Procedure score of 66.60 ± 18.64 points. The average blocks moved in the Box and Block Test were 20.80 ± 7.46, and the mean score in the Action Research Arm Test was 37.20 ± 5.45. The mean time for the Clothespin-Relocation Test was 26.90 ± 11.61 seconds. The patients reported a wearing time of an average of 12.80 ± 3.11 hours per day. The mean number of prosthetic motions performed each day was 257.23 ± 192.95 with a range from 23.07 to 489.13. CONCLUSIONS Neither high functionality nor long wearing times necessitated frequent use of a prosthesis in daily life. However, frequent daily motions did translate into good functional scores, indicating that regular device use in different real-life settings relates to functionality.
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Affiliation(s)
- Stefan Salminger
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
- Clinical Laboratory for Bionic Extremity Reconstruction, Medical University of Vienna, Vienna, Austria
| | - Clemens Gstoettner
- Clinical Laboratory for Bionic Extremity Reconstruction, Medical University of Vienna, Vienna, Austria
| | - Agnes Sturma
- Clinical Laboratory for Bionic Extremity Reconstruction, Medical University of Vienna, Vienna, Austria
- Department of Bioengineering, Imperial College London, London, UK
| | - Johannes A Mayer
- Clinical Laboratory for Bionic Extremity Reconstruction, Medical University of Vienna, Vienna, Austria
| | - Helmut Papst
- Otto Bock Healthcare Products GmbH, Vienna, Austria
| | - Oskar C Aszmann
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
- Clinical Laboratory for Bionic Extremity Reconstruction, Medical University of Vienna, Vienna, Austria
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4
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Osborn LE, Moran C, Dodd LD, Sutton E, Norena Acosta N, Wormley J, Pyles CO, Gordge KD, Nordstrom M, Butkus J, Forsberg JA, Pasquina P, Fifer MS, Armiger RS. Monitoring at-home prosthesis control improvements through real-time data logging. J Neural Eng 2022; 19. [PMID: 35523131 DOI: 10.1088/1741-2552/ac6d7b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 05/06/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users. APPROACH A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration (OI) to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data. MAIN RESULT The participant's continuous prosthesis usage steadily increased (p = 0.04, max = 5.5 hr) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time (p = 0.04), resulting in up to 5.4 hr of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week, p < 0.001) with a maximum number of 10 classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage (p < 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control (ACMC) scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index (NASA-TLX) scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study. SIGNIFICANCE In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.
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Affiliation(s)
- Luke E Osborn
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Courtney Moran
- Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Lauren D Dodd
- Henry M Jackson Foundation for the Advancement of Military Medicine, 6720A Rockledge Dr, Bethesda, Maryland, 20817, UNITED STATES
| | - Erin Sutton
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Nicolas Norena Acosta
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Jared Wormley
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Connor O Pyles
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Kelles D Gordge
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Michelle Nordstrom
- Department of Rehabilitation, Walter Reed National Military Medical Center, 4494 Palmer Rd N, Bethesda, 20889, UNITED STATES
| | - Josef Butkus
- Department of Rehabilitation, Walter Reed National Military Medical Center, 4494 Palmer Rd N, Bethesda, 20889, UNITED STATES
| | - Jonathan A Forsberg
- Department of Orthopaedic Surgery, Johns Hopkins Medicine, 1800 Orleans St, Baltimore, Maryland, 21287, UNITED STATES
| | - Paul Pasquina
- Department of Rehabilitation, Walter Reed National Military Medical Center, 4494 Palmer Rd N, Bethesda, Maryland, 20814, UNITED STATES
| | - Matthew S Fifer
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
| | - Robert S Armiger
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, Maryland, 20723, UNITED STATES
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5
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Osborn LE, Moran CW, Johannes MS, Sutton EE, Wormley JM, Dohopolski C, Nordstrom MJ, Butkus JA, Chi A, Pasquina PF, Cohen AB, Wester BA, Fifer MS, Armiger RS. Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency. J Neural Eng 2021; 18. [PMID: 33524965 DOI: 10.1088/1741-2552/abe20d] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/01/2021] [Indexed: 01/21/2023]
Abstract
Objective.Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality.Approach.Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI).Main results.Throughout the study, continuous prosthesis usage increased (1% per week,p< 0.001) and functional metrics improved up to 26% on control assessments and 76% on perceived workload evaluations. We observed increases in torque loading on the OI implant (up to 12.5% every month,p< 0.001) and prosthesis control performance (0.5% every month,p< 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (p< 0.001), over time without degrading performance, demonstrating improved control efficiency with a machine learning-based myoelectric pattern recognition algorithm. The participant controlled the prosthesis up to one month without updating the pattern recognition algorithm. The participant customized prosthesis movements to perform specific tasks, such as individual finger control for piano playing and hand gestures for communication, which likely contributed to continued usage.Significance.This work demonstrates, in a single participant, the functional benefit of unconstrained use of a highly anthropomorphic prosthetic limb over an extended period. While hurdles remain for widespread use, including device reliability, results replication, and technical maturity beyond a prototype, this study offers insight as an example of the impact of advanced prosthesis technology for rehabilitation outside the laboratory.
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Affiliation(s)
- Luke E Osborn
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Courtney W Moran
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Matthew S Johannes
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Erin E Sutton
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Jared M Wormley
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Christopher Dohopolski
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Michelle J Nordstrom
- Department of Rehabilitation, Walter Reed National Military Medical Center, Bethesda, MD, United States of America.,Department of Physical Medicine and Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America.,Center for Rehabilitation Sciences Research (CRSR), Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Josef A Butkus
- Department of Rehabilitation, Walter Reed National Military Medical Center, Bethesda, MD, United States of America
| | - Albert Chi
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America.,Department of Surgery, Oregon Health & Science University, Portland, OR, United States of America
| | - Paul F Pasquina
- Department of Rehabilitation, Walter Reed National Military Medical Center, Bethesda, MD, United States of America.,Department of Physical Medicine and Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America.,Center for Rehabilitation Sciences Research (CRSR), Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Adam B Cohen
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Brock A Wester
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Matthew S Fifer
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Robert S Armiger
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
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6
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Wu H, Dyson M, Nazarpour K. Arduino-Based Myoelectric Control: Towards Longitudinal Study of Prosthesis Use. SENSORS 2021; 21:s21030763. [PMID: 33498801 PMCID: PMC7866037 DOI: 10.3390/s21030763] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/16/2021] [Accepted: 01/20/2021] [Indexed: 11/16/2022]
Abstract
Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications. In this work, we introduce the design of a cost-effective Arduino-based myoelectric control system with wearable electromyogram (EMG) sensors. The design considerations focused on home studies, so the robustness, user-friendly control adjustments, and user supports were the main concerns. Three control algorithms, namely, direct control, abstract control, and linear discriminant analysis (LDA) classification, were implemented in the system. In this paper, we will share our design principles and report the robustness of the system in continuous operation in the laboratory. In addition, we will show a first real-time implementation of the abstract decoder for prosthesis control with an able-bodied participant.
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Affiliation(s)
- Hancong Wu
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9YL, UK
- Correspondence: (H.W.); (K.N.)
| | - Matthew Dyson
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Kianoush Nazarpour
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9YL, UK
- Correspondence: (H.W.); (K.N.)
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7
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Chadwell A, Diment L, Micó-Amigo M, Morgado Ramírez DZ, Dickinson A, Granat M, Kenney L, Kheng S, Sobuh M, Ssekitoleko R, Worsley P. Technology for monitoring everyday prosthesis use: a systematic review. J Neuroeng Rehabil 2020; 17:93. [PMID: 32665020 PMCID: PMC7362458 DOI: 10.1186/s12984-020-00711-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/23/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring, with particular focus on low-resource settings. METHODS A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis users during daily-living. RESULTS Sixty lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm. CONCLUSIONS Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user's requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.
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Affiliation(s)
| | - Laura Diment
- People Powered Prosthetics Group, University of Southampton, Southampton, UK
| | - M Micó-Amigo
- People Powered Prosthetics Group, University of Southampton, Southampton, UK
| | | | - Alex Dickinson
- People Powered Prosthetics Group, University of Southampton, Southampton, UK.
- Exceed Research Network, Exceed Worldwide, Lisburn, UK.
| | - Malcolm Granat
- University of Salford, Salford, UK
- Exceed Research Network, Exceed Worldwide, Lisburn, UK
| | - Laurence Kenney
- University of Salford, Salford, UK
- Exceed Research Network, Exceed Worldwide, Lisburn, UK
| | - Sisary Kheng
- University of Salford, Salford, UK
- Exceed Worldwide, Phnom Penh, Cambodia
| | | | | | - Peter Worsley
- People Powered Prosthetics Group, University of Southampton, Southampton, UK
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8
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Simon AM, Turner KL, Miller LA, Hargrove LJ, Kuiken TA. Pattern recognition and direct control home use of a multi-articulating hand prosthesis. IEEE Int Conf Rehabil Robot 2020; 2019:386-391. [PMID: 31374660 DOI: 10.1109/icorr.2019.8779539] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Although more multi-articulating hand prostheses have become commercially available, replacing a missing hand remains challenging from a control perspective. This study investigated myoelectric direct control and pattern recognition home use of a multi-articulating hand prosthesis for individuals with a transradial amputation. Four participants were fitted with an i-limb Ultra Revolution hand and a Coapt COMPLETE CONTROL system. An occupational therapist provided training for each control style and how to use the various grips. The number of grips available to each individual was determined by clinician and user feedback to optimize both the number of grips available and the reliability of grip selection. Home trial data corresponding to individual usage were recorded. No significant differences were found between direct and pattern recognition control home trials in regards to trial length (p=0.96), days powered on (p=0.21), or total time powered on (p=0.91). There was a higher average number of configured grips for direct control at 4.8 [0.5] compared to 3.8 [0.5] for pattern recognition control, but this difference did not reach significance (p=0.092). Across all hand close movements, users spent a majority of time $(\gt80$%) in one grip when using direct control. For pattern recognition usage was spread across more grips $(\gt45$% time in one grip, 25% time in a 2nd grip, and 20% time in a 3rd grip). Pattern recognition control may provide users with a more intuitive way to select and use the various grips available to them.
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9
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Pena AE, Rincon-Gonzalez L, Abbas JJ, Jung R. Effects of vibrotactile feedback and grasp interface compliance on perception and control of a sensorized myoelectric hand. PLoS One 2019; 14:e0210956. [PMID: 30650161 PMCID: PMC6334959 DOI: 10.1371/journal.pone.0210956] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 01/04/2019] [Indexed: 11/18/2022] Open
Abstract
Current myoelectric prosthetic limbs are limited in their ability to provide direct sensory feedback to users, which increases attentional demands and reliance on visual cues. Vibrotactile sensory substitution (VSS), which can be used to provide sensory feedback in a non-invasive manner has demonstrated some improvement in myoelectric hand control. In this work, we developed and tested two VSS configurations: one with a single burst-rate modulated actuator and another with a spatially distributed array of five coin tactors. We performed a direct comparative assessment of these two VSS configurations with able-bodied subjects to investigate sensory perception, myoelectric control of grasp force and hand aperture with a prosthesis, and the effects of interface compliance. Six subjects completed a sensory perception experiment under a stimulation only paradigm; sixteen subjects completed experiments to compare VSS performance on perception and graded myoelectric control during grasp force and hand aperture tasks; and ten subjects completed experiments to investigate the effect of mechanical compliance of the myoelectric hand on the ability to control grasp force. Results indicated that sensory perception of vibrotactile feedback was not different for the two VSS configurations in the absence of active myoelectric control, but it was better with feedback from the coin tactor array than with the single actuator during myoelectric control of grasp force. Graded myoelectric control of grasp force and hand aperture was better with feedback from the coin tactor array than with the single actuator, and myoelectric control of grasp force was improved with a compliant grasp interface. Further investigations with VSS should focus on the use of coin tactor arrays by subjects with amputation in real-world settings and on improving control of grasp force by increasing the mechanical compliance of the hand.
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Affiliation(s)
- Andres E. Pena
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of America
| | - Liliana Rincon-Gonzalez
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of America
| | - James J. Abbas
- School of Biological & Health Systems Engineering, Arizona State University, Tempe, AZ, United States of America
| | - Ranu Jung
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of America
- * E-mail:
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