Literature DB >> 34308631

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion.

Xinge Guo1,2,3,4, Tianyiyi He1,2,3, Zixuan Zhang1,2,3, Anxin Luo5, Fei Wang5, Eldwin J Ng4, Yao Zhu4, Huicong Liu6, Chengkuo Lee1,2,3,7.   

Abstract

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

Entities:  

Keywords:  Internet of Things; artificial intelligence; energy harvesting; triboelectric; walking stick

Mesh:

Year:  2021        PMID: 34308631     DOI: 10.1021/acsnano.1c04464

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  6 in total

Review 1.  Morphological Engineering of Sensing Materials for Flexible Pressure Sensors and Artificial Intelligence Applications.

Authors:  Zhengya Shi; Lingxian Meng; Xinlei Shi; Hongpeng Li; Juzhong Zhang; Qingqing Sun; Xuying Liu; Jinzhou Chen; Shuiren Liu
Journal:  Nanomicro Lett       Date:  2022-07-05

2.  Raindrop energy-powered autonomous wireless hyetometer based on liquid-solid contact electrification.

Authors:  Chaoqun Xu; Xianpeng Fu; Chengyu Li; Guoxu Liu; Yuyu Gao; Youchao Qi; Tianzhao Bu; Yuanfen Chen; Zhong Lin Wang; Chi Zhang
Journal:  Microsyst Nanoeng       Date:  2022-03-14       Impact factor: 7.127

3.  Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Authors:  Quan Zhang; Tao Jin; Jianguo Cai; Liang Xu; Tianyiyi He; Tianhong Wang; Yingzhong Tian; Long Li; Yan Peng; Chengkuo Lee
Journal:  Adv Sci (Weinh)       Date:  2021-11-19       Impact factor: 16.806

4.  Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions.

Authors:  Zhongda Sun; Minglu Zhu; Xuechuan Shan; Chengkuo Lee
Journal:  Nat Commun       Date:  2022-09-05       Impact factor: 17.694

5.  Application and Prospect Analysis of Artificial Intelligence in the Field of Physical Education.

Authors:  Wujun Xiang
Journal:  Comput Intell Neurosci       Date:  2022-08-13

6.  Design and Experimental Investigation of a Rotational Piezoelectric Energy Harvester with an Offset Distance from the Rotation Center.

Authors:  Jun Chen; Xiangfu Liu; Hengyang Wang; Sheng Wang; Mingjie Guan
Journal:  Micromachines (Basel)       Date:  2022-02-28       Impact factor: 2.891

  6 in total

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