Hou Lei, Latif Jawwad, Mehryar Pouyan, Withers Stephen, Plastropoulos Angelos, Shen Linlin, Ali Zulfiqur
Healthcare Innovation Centre, School of Health & Life Sciences, Teesside University, Middlesbrough, TS1 BX, UK.
Zhejiang Lab, Research Center for Frontier Fundamental Studies, Hangzhou, 311121, China.
Sci Rep. 2024 Mar 11;14(1):5878. doi: 10.1038/s41598-024-56357-y.
Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.
助力电动轮椅将使患者和老年人在无需护理人员直接干预的情况下保持行动能力。可以远程收集和分析用户的生命体征，以便更好地预防疾病并对健康和慢性病进行主动管理。本研究提出了一种基于物联网（IoT）集成生物物理传感器的自主轮椅原型系统。开发了一种电动轮椅系统，配备三个生物物理传感器，用于收集、传输和分析用户的四项生命体征，为用户和临床医生提供实时反馈。开发了一个嵌入云人工智能（AI）算法的用户界面软件，用于数据可视化和分析。提出了一种改进的数据压缩算法——简约、自适应和流式R位（O-MAS-R），在数据传输过程中与MAS算法相比，实现了更高的压缩率，最小为7.1%，最大为45.25%。同时，原型轮椅与智能轮椅应用程序相结合，实时吸收来自车载传感器的数据和周围环境的特征，实现包括障碍物激光扫描、自主定位以及在预定义区域内的点对点路线规划和移动等功能。总之，轮椅原型使用人工智能算法和导航技术，帮助患者和老年人保持独立行动能力并实时监测他们的医疗信息。
