1
|
Shimizu Y, Mori T, Yoshikawa K, Katane D, Torishima H, Hara Y, Yozu A, Yamazaki M, Hada Y, Mutsuzaki H. Developing a Novel Prosthetic Hand with Wireless Wearable Sensor Technology Based on User Perspectives: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2765. [PMID: 38732871 PMCID: PMC11086240 DOI: 10.3390/s24092765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/13/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
Myoelectric hands are beneficial tools in the daily activities of people with upper-limb deficiencies. Because traditional myoelectric hands rely on detecting muscle activity in residual limbs, they are not suitable for individuals with short stumps or paralyzed limbs. Therefore, we developed a novel electric prosthetic hand that functions without myoelectricity, utilizing wearable wireless sensor technology for control. As a preliminary evaluation, our prototype hand with wireless button sensors was compared with a conventional myoelectric hand (Ottobock). Ten healthy therapists were enrolled in this study. The hands were fixed to their forearms, myoelectric hand muscle activity sensors were attached to the wrist extensor and flexor muscles, and wireless button sensors for the prostheses were attached to each user's trunk. Clinical evaluations were performed using the Simple Test for Evaluating Hand Function and the Action Research Arm Test. The fatigue degree was evaluated using the modified Borg scale before and after the tests. While no statistically significant differences were observed between the two hands across the tests, the change in the Borg scale was notably smaller for our prosthetic hand (p = 0.045). Compared with the Ottobock hand, the proposed hand prosthesis has potential for widespread applications in people with upper-limb deficiencies.
Collapse
Affiliation(s)
- Yukiyo Shimizu
- Department of Rehabilitation Medicine, Institute of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan
- Ibaraki Prefectural University of Health Sciences Hospital, Ami 300-0331, Japan
| | - Takahiko Mori
- Department of Electrical and Electronic Engineering, Shonan Institute of Technology, Fujisawa 251-8511, Japan
| | - Kenichi Yoshikawa
- Ibaraki Prefectural University of Health Sciences Hospital, Ami 300-0331, Japan
| | - Daisuke Katane
- Ibaraki Prefectural University of Health Sciences Hospital, Ami 300-0331, Japan
| | - Hiroyuki Torishima
- Saitama Prosthetics and Orthotics Manufacturing Service Co., Ltd., Saitama 337-0051, Japan
| | - Yuki Hara
- Department of Neurophysiology, National Center of Neurology and Psychiatry, Kodaira 187-8551, Japan
| | - Arito Yozu
- Department of Precision Engineering, University of Tokyo, Bunkyo 113-8656, Japan
| | - Masashi Yamazaki
- Department of Orthopaedic Surgery, Institute of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan
| | - Yasushi Hada
- Department of Rehabilitation Medicine, Institute of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan
| | - Hirotaka Mutsuzaki
- Ibaraki Prefectural University of Health Sciences Hospital, Ami 300-0331, Japan
- Center for Medical Science, Ibaraki Prefectural University of Health Sciences, Ami 300-0331, Japan
| |
Collapse
|
2
|
Bao T, Xie SQ, Yang P, Zhou P, Zhang ZQ. Towards Robust, Adaptive and Reliable Upper-limb Motion Estimation Using Machine Learning and Deep Learning--A Survey in Myoelectric Control. IEEE J Biomed Health Inform 2022; 26:3822-3835. [PMID: 35294368 DOI: 10.1109/jbhi.2022.3159792] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
To develop multi-functional human-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. \textcolor{red}{Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.
Collapse
|
3
|
Bloom J, Hejrati B. The effects of forearm movements on human gait during walking with various self-selected speeds. Hum Mov Sci 2021; 79:102835. [PMID: 34265508 DOI: 10.1016/j.humov.2021.102835] [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: 07/14/2020] [Revised: 05/29/2021] [Accepted: 06/29/2021] [Indexed: 11/30/2022]
Abstract
The forearms significantly contribute to the upper extremity movements and, consequently, whole-body responses during locomotion. The purpose of this study is to provide a more in-depth understanding of the mechanism controlling forearm movements during walking by comprehensively investigating the effects of the forearms on the lower and upper limb movements. Such an understanding can provide critical information for the design and control of robotic upper-limb prostheses. Twelve healthy young participants were recruited to compare their gait during (1) natural walking, (2) walking while wearing a pair of artificial passive forearms and having their actual forearms restrained by orthopedic braces, and (3) walking with only having their forearms restrained by the braces (i.e., no artificial forearms). While the passive forearms in condition 2 were to determine if the forearm movements were passively or actively controlled, condition 3 was to account for the effects of restraining the forearms in condition 2. The participants' lower-limb joint angles and spatiotemporal parameters remained unchanged across the three conditions while walking at their normal and fast self-selected gait speeds. However, significant decreases were observed in the shoulder and trunk angles, the interlimb coordination, and the shoulder-trunk correlations when walking with the artificial forearms. These observations were in tandem with the increased muscle activity of the biceps, trapeziuses, and posterior deltoids, which controlled the shoulder motion and trunk rotation during walking with the artificial forearms across both normal and fast self-selected speeds. Although not significant, the metabolic energy analysis of five participants revealed an increase during walking with artificial forearms. The results support the idea that the body actively controls the forearm movements through the shoulder and trunk rotations to mitigate the undesired disturbances induced by the passive forearm movements during locomotion.
Collapse
Affiliation(s)
- Jacob Bloom
- Biorobotics and Biomechanics Lab, Department of Mechanical Engineering, University of Maine, Orono, ME, United States of America.
| | - Babak Hejrati
- Biorobotics and Biomechanics Lab, Department of Mechanical Engineering, University of Maine, Orono, ME, United States of America.
| |
Collapse
|
4
|
Khan SM, Khan AA, Farooq O. EMG based classification for pick and place task. Biomed Phys Eng Express 2021; 7. [PMID: 33882462 DOI: 10.1088/2057-1976/abfa81] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/21/2021] [Indexed: 11/12/2022]
Abstract
The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.
Collapse
Affiliation(s)
- Salman Mohd Khan
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
| | - Abid Ali Khan
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
| | - Omar Farooq
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
| |
Collapse
|
5
|
AlMohimeed I, Ono Y. Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor. SENSORS 2020; 20:s20133616. [PMID: 32605006 PMCID: PMC7374409 DOI: 10.3390/s20133616] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 12/25/2022]
Abstract
Skeletal muscle is considered as a near-constant volume system, and the contractions of the muscle are related to the changes in tissue thickness. Assessment of the skeletal muscle contractile parameters such as maximum contraction thickness (Th), contraction time (Tc), contraction velocity (Vc), sustain time (Ts), and half-relaxation (Tr) provides valuable information for various medical applications. This paper presents a single-element wearable ultrasonic sensor (WUS) and a method to measure the skeletal muscle contractile parameters in A-mode ultrasonic data acquisition. The developed WUS was made of double-layer polyvinylidene fluoride (PVDF) piezoelectric polymer films with a simple and low-cost fabrication process. A flexible, lightweight, thin, and small size WUS would provide a secure attachment to the skin surface without affecting the muscle contraction dynamics of interest. The developed WUS was employed to monitor the contractions of gastrocnemius (GC) muscle of a human subject. The GC muscle contractions were evoked by the electrical muscle stimulation (EMS) at varying EMS frequencies from 2 Hz up to 30 Hz. The tissue thickness changes due to the muscle contractions were measured by utilizing a time-of-flight method in the ultrasonic through-transmission mode. The developed WUS demonstrated the capability to monitor the tissue thickness changes during the unfused and fused tetanic contractions. The tetanic progression level was quantitatively assessed using the parameter of the fusion index (FI) obtained. In addition, the contractile parameters (Th, Tc, Vc, Ts, and Tr) were successfully extracted from the measured tissue thickness changes. In addition, the unfused and fused tetanus frequencies were estimated from the obtained FI-EMS frequency curve. The WUS and ultrasonic method proposed in this study could be a valuable tool for inexpensive, non-invasive, and continuous monitoring of the skeletal muscle contractile properties.
Collapse
Affiliation(s)
- Ibrahim AlMohimeed
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada;
- Department of Medical Equipment Technology, Majmaah University, Majmaah 11952, Saudi Arabia
| | - Yuu Ono
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada;
- Correspondence:
| |
Collapse
|
6
|
Khan SM, Khan AA, Farooq O. Selection of Features and Classifiers for EMG-EEG-Based Upper Limb Assistive Devices-A Review. IEEE Rev Biomed Eng 2019; 13:248-260. [PMID: 31689209 DOI: 10.1109/rbme.2019.2950897] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Bio-signals are distinctive factors in the design of human-machine interface, essentially useful for prosthesis, orthosis, and exoskeletons. Despite the progress in the analysis of pattern recognition based devices; the acceptance of these devices is still questionable. One reason is the lack of information to identify the possible combinations of features and classifiers. Besides; there is also a need for optimal selection of various sensors for sensations such as touch, force, texture, along with EMGs/EEGs. This article reviews the two bio-signal techniques, named as electromyography and electroencephalography. The details of the features and the classifiers used in the data processing for upper limb assist devices are summarised here. Various features and their sets are surveyed and different classifiers for feature sets are discussed on the basis of the classification rate. The review was carried out on the basis of the last 10-12 years of published research in this area. This article also outlines the influence of modality of EMGs and EEGs with other sensors on classifications. Also, other bio-signals used in upper limb devices and future aspects are considered.
Collapse
|