Contactless tracking of humans using non-contact triboelectric sensing technology: Enabling new assistive applications for the elderly and the visually impaired

摩擦电效应 可穿戴计算机 可穿戴技术 康复 灵敏度(控制系统) 计算机科学 持续监测 人机交互 模拟 物理医学与康复 嵌入式系统 材料科学 医学 工程类 物理疗法 电子工程 运营管理 复合材料
作者
David Vera Anaya,Ke Zhan,Li Tao,Chengkuo Lee,Mehmet Rasit Yuce,Tuncay Alan
出处
期刊:Nano Energy [Elsevier]
卷期号:90: 106486-106486 被引量:72
标识
DOI:10.1016/j.nanoen.2021.106486
摘要

Continuous monitoring of human motion and its interaction with the surrounding environment is crucial for healthcare applications such as early diagnosis of neurological disorders, or rehabilitation of vulnerable patients with walking difficulties in hospital and age-care settings. Additionally, it is also necessary to provide assistive care to visually impaired individuals who are prone to falls. Commonly used wearable sensors require on-body components, which may cause discomfort. More importantly, the forgetfulness of people in hospital or aged-care settings or their resistance in using the on-body units, undermine the effectiveness of a wearable-based monitoring strategy. The ideal monitoring system should not interfere with the individual’s privacy and daily routines and work continuously, without requiring any specific on-body devices. Here, we present a contactless sensing platform that can monitor and distinguish complex movements of human subjects, without requiring them to use wearable devices. The approach uses flexible Non-Contact Triboelectric Sensors (NCTS) (7 ×3.6 ×0.5 cm3) produced by attaching a negatively pre-charged PDMS film onto an aluminum (Al) foil. By exploiting the sensitivity to external electrostatic charges, near-field remote monitoring of human movements at distances as far as 1.5 m, is successfully achieved. Furthermore, The NCTS system differentiates activities such as walking, running, and jumping; and estimates walking speed, relative position, and direction of two people walking together in a contactless manner, which is not well examined in the literature of Non-contact Triboelectric Sensors, by analyzing the time domain and frequency spectrum of the output signal. Besides, the sensor is integrated with a portable accident prevention system designed to avoid visually impaired people’s collisions with obstacles. Falling detection tests provide promising results for their application in elderly safety. Finally, indoor navigation/position monitoring and direction recognition are achieved and demonstrated using multiple NCTS, for future potential applications in elder activity tracking and people counting in hospitals.
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