The integration of sensor technology into mechanical prosthetics has revolutionized the field, allowing for precise control and natural movement. Sensors provide valuable feedback to clinicians, aiding in the customization and optimization of prosthetic devices for individual patient needs. This amalgamation of sensor technology and mechanical prosthetics represents a significant breakthrough, showcasing the potential of technological innovation to transform treatment modalities and restore functionality to those in need. The enhanced mobility afforded by sensor-driven prosthetics improves patient quality of life and independence. Clinicians benefit from real-time data insights, enabling them to make informed decisions and adjustments to optimize prosthetic performance. Customization ensures a personalized fit and function, addressing unique patient requirements. This transformative approach underscores the impact of advancing technology in healthcare, particularly in improving mobility solutions for individuals with limb impairments.
Top1. Introduction
Mechanical prosthetics have long been instrumental in restoring mobility and functionality for individuals with limb loss. However, traditional prosthetic devices often lack the natural movement and intuitive control found in biological limbs. Recent advancements in sensor-driven technologies have revolutionized the field of mechanical prosthetics, offering unprecedented opportunities to enhance mobility and improve user experience. This chapter explores the transformative role of sensor-driven innovations in mechanical prosthetics, highlighting their potential to empower individuals with limb loss and revolutionize the field of assistive technology.
This chapter highlights an advancement in upper limb prosthetic devices, including sensor-driven innovations, and discusses their implications for rehabilitation. It highlights the role of sensors in enhancing mobility, control, and functionality in upper limb prosthetics.
The impact of adaptive prosthetic knee control on functional performance during rehabilitation therapy was presented in several studies and demonstrates how sensor-driven innovations improve mobility and gait dynamics in individuals with lower limb amputations. Previous studies explores the needs and preferences of upper limb prosthesis users, including the importance of sensor-driven innovations in enhancing mobility, dexterity, and usability of prosthetic devices. Sensor-driven innovations in mechanical prosthetics emphasizes the role of sensor-driven innovations in improving mobility and enhancing user satisfaction with prosthetic devices. It provides user perspectives on prosthetic usage in everyday life activities, mobility, and participation. The integration of wearable sensors into prosthetic devices enhances mobility, movement analysis, and rehabilitation outcomes. Electromyogram (EMG) pattern recognition has been used in the past for the control of powered upper-limb prostheses. EMG-based control systems has the potential of improving mobility and functionality in upper limb prosthetics. Surface electromyographic signal classification methods has been presented by the researcher for the simultaneous control of a shoulder abduction and elbow flexion prosthesis. It explores the effectiveness of EMG-based control strategies in enhancing mobility and coordination in upper limb prosthetics. There are several emerging avenues and challenges in using EMG-based control systems to improve mobility and functionality in upper limb prosthetics. This chapter discusses the challenges/innovations in extraction of neural information from surface electromyography (EMG) signals for the control of upper-limb prostheses. The design and validation of a portable, inexpensive, and customizable dynamic prosthetic socket has been investigated by Gubbala and Inala (Gubbala & Inala, 2021) and had demonstrated the sensor-driven innovations in socket design to improve comfort, fit, and mobility for prosthetic users (Bhowmick, Parijat & Das, Sima & Mazumdar, 2023; Das, 2023; Das, Adhikary, et al., 2023; Das, Balmiki, et al., 2023; Gubbala & Inala, 2021; Mazumdar & Das, 2023; Patel et al., 2012a, 2012b; Resnik et al., 2012; Theeven et al., 2012; Tsai et al., 2014) .