Literature Review on Needs of Upper Limb Prosthesis Users

Abstract

The loss of one hand can significantly affect the level of autonomy and the capability of performing daily living, working and social activities. The current prosthetic solutions contribute in a poor way to overcome these problems due to limitations in the interfaces adopted for controlling the prosthesis and to the lack of force or tactile feedback, thus limiting hand grasp capabilities. This paper presents a literature review on needs analysis of upper limb prosthesis users, and points out the main critical aspects of the current prosthetic solutions, in terms of users satisfaction and activities of daily living they would like to perform with the prosthetic device. The ultimate goal is to provide design inputs in the prosthetic field and, contemporary, increase user satisfaction rates and reduce device abandonment. A list of requirements for upper limb prostheses is proposed, grounded on the performed analysis on user needs. It wants to (i) provide guidelines for improving the level of acceptability and usefulness of the prosthesis, by accounting for hand functional and technical aspects; (ii) propose a control architecture of PNS-based prosthetic systems able to satisfy the analyzed user wishes; (iii) provide hints for improving the quality of the methods (e.g., questionnaires) adopted for understanding the user satisfaction with their prostheses.

EMG-Controlled Transradial Prostheses - An Investigation into Machine Learning Techniques

“…The EMG signals have been used by researcher and clinicians for decades to investigate muscles in healthy as well as pathological conditions [23], [26], [1]. Recently, owing to the advancements in machine learning and pattern classification techniques, the EMG signals are being used extensively to control prosthetic devices [6], [4], [27], [24], [2]. These prosthetic devices are externally powered and are actuated using the intended movement information produced by these machine learning algorithms [6], [25].…”

“…These prosthetic devices are externally powered and are actuated using the intended movement information produced by these machine learning algorithms [6], [25]. Generally, the surface EMG signals are recorded from leftover muscles of amputees and after the necessary preprocessing representative features are extracted [7], [6]. Using feature data, a supervised or unsupervised machine learning algorithm is trained [16], [7].…”

“…Recently, owing to the advancements in machine learning and pattern classification techniques, the EMG signals are being used extensively to control prosthetic devices [6], [4], [27], [24], [2]. These prosthetic devices are externally powered and are actuated using the intended movement information produced by these machine learning algorithms [6], [25]. Generally, the surface EMG signals are recorded from leftover muscles of amputees and after the necessary preprocessing representative features are extracted [7], [6].…”

Introductory Chapter: Toward Near-Natural Assistive Devices

“…The most universally desired characteristics among all groups are reductions in cost and weight. Overall, ADL tasks that people rank as most important include mobility, object handling, and manipulation [14].…”

Human-in-the-loop assistive cyber physical system control using physiological signals

“…It has been noted that myoelectric users pay more attention to function and comfort [27] as well as difficulty of adapting grasping configuration of the prosthesis to the target objects shape [28]. Moreover, the main goal of a prosthesis is to assist users in performing their daily activities [29]. In this work, we propose an architecture to extract information from the user's environment to assist in the intent inference, thus making the overall system more robust and reliable.…”