Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices

Stefanou, Thekla; Chance, Greg; Assaf, Tareq; Dogramadzi, Sanja

doi:10.3389/frobt.2019.00124

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…SVM achieved the highest recognition accuracy in real-time mode, although it required the longest training and prediction time compared to kNN and DT methods [77]. SVM has also outperformed kNN, logistic regression and RF methods for recognition of grip action from an assistive tactile arm brace (TAB) worn on the forearm of participants [78]. However, in human activity recognition (HAR) with IMU data, RF methods showed to perform better compared to SVM, DT, NN, kNN and Naive Bayes methods [79].…”

Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Martinez-Hernandez

Metcalfe

Assaf

et al. 2021

Sensors

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Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

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Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Martinez-Hernandez

Metcalfe

Assaf

et al. 2021

Sensors

Self Cite

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“…A review of the databases showed that there is no comparable software; hence, it is difficult to compare our results with those of other authors. A review of four key databases, PubMed, WoS, Scopus, and Google Scholar, returned only four scientific articles in English with the keywords 'assistive device', 'stroke', and 'machine learning' [13][14][15][16]. The introduction of a second opinion or expert system into the selection process of assistive technologies, not only in post-stroke patients, seems to be a necessity, as this has been a research gap thusfar.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

et al. 2023

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It is increasingly important to provide post-stroke patients with rapid access to patient-tailored assistive technologies to increase independence, mobility, and participation. Automating the selection of assistive devices based on artificial intelligence could speed up the process and improve accuracy. It would also relieve the burden on diagnosticians and therapists and speed up the introduction of new ranges by automating databases. This article compares selected machine learning classification methods in the area of post-stroke rehabilitation device selection. The article covers the specifics of the selection, the choice of classification methods, and the identification of the best one, as well as the experimental part, the description of the results, the comparison process, and directions for further research. The novelty lies both in the topic, as the choice of classification method has an impact on the accuracy of classification in the selection of medical materials, and in the manner of the comprehensive approach. The possible contribution is of great scientific and clinical relevance, but above all, it has economic and social importance, enabling post-stroke individuals to return more quickly to the community, learning, and work, and relieving the burden on the health care system.

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“… An example of FMG device with an array of eight force sensors (Tactile Arm Brace) placed on the able-bodied user. Adapted from [ 101 ], licensed under CC BY 4.0. …”

Section: Figurementioning

confidence: 99%

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

Grushko

Spurný

Černý

2020

Sensors

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The loss of a hand can significantly affect one’s work and social life. For many patients, an artificial limb can improve their mobility and ability to manage everyday activities, as well as provide the means to remain independent. This paper provides an extensive review of available biosensing methods to implement the control system for transradial prostheses based on the measured activity in remnant muscles. Covered techniques include electromyography, magnetomyography, electrical impedance tomography, capacitance sensing, near-infrared spectroscopy, sonomyography, optical myography, force myography, phonomyography, myokinetic control, and modern approaches to cineplasty. The paper also covers combinations of these approaches, which, in many cases, achieve better accuracy while mitigating the weaknesses of individual methods. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each technique along with their relationship with proprioceptive feedback, which is an important factor for intuitive control over the prosthetic device, especially for high dexterity prosthetic hands.

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Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices

Cited by 7 publications

References 34 publications

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

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