Assistive Living and Wireless Emergency Alert System for Mobility Impaired Users

This study presents a flexible, multielement, touch sensitive patch, designed for assistive living applications. The patch has a pressure sensitivity of 0.826 kPa<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>, response time 600 ms, and only 11% drop in performance when bent at 30 cm radius of curvature. Output from the patch was given to a machine learning algorithm, designed to run on a low powered microcontroller for on-device computing. The system recognizes different touch gestures with 92% accuracy and triggers specific tasks, such as switching on/off the light or locking/unlocking the door remotely. It also acts as an alarm system that sends an SOS signal to the nearby device wirelessly, in case of emergencies. The device offers a promising solution for mobility-impaired individuals, such as the elderly or bedridden patients, empowering them to manage their day-to-day tasks independently and interact with their surroundings without minimal effort.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/fleps65444.2025.11105667
Citations
2
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
2.55
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article

Assistive Living and Wireless Emergency Alert System for Mobility Impaired Users

Shrutidhara Sarma, Eugen Koch, Andreas H. Dietzel, Gitansh Verma
2 citations
Context-Aware Activity Recognition Systems
2.55
article

Assistive Living and Wireless Emergency Alert System for Mobility Impaired Users

Shrutidhara Sarma, Eugen Koch, Andreas H. Dietzel, Gitansh Verma
article en
2 citations

Abstract

This study presents a flexible, multielement, touch sensitive patch, designed for assistive living applications. The patch has a pressure sensitivity of 0.826 kPa<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>, response time 600 ms, and only 11% drop in performance when bent at 30 cm radius of curvature. Output from the patch was given to a machine learning algorithm, designed to run on a low powered microcontroller for on-device computing. The system recognizes different touch gestures with 92% accuracy and triggers specific tasks, such as switching on/off the light or locking/unlocking the door remotely. It also acts as an alarm system that sends an SOS signal to the nearby device wirelessly, in case of emergencies. The device offers a promising solution for mobility-impaired individuals, such as the elderly or bedridden patients, empowering them to manage their day-to-day tasks independently and interact with their surroundings without minimal effort.

Indian Institute of Technology Jodhpur (IN), Technische Universität Braunschweig (DE)
Openalex Percentile: Top 9%
Context-Aware Activity Recognition Systems
2.55
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Assistive Living and Wireless Emergency Alert System for Mobility Impaired Users — Shrutidhara Sarma, Eugen Koch, et al. · (2025) | TGRS Research Map | TGRS