IoT and Machine Learning for Supporting Personal Mobility in the Elderly

Abstract

The rapid advancements in Internet of Things (IoT) and Machine Learning (ML) technologies have opened new frontiers in enhancing the quality of life for the elderly, particularly in supporting personal mobility. This paper explores the integration of IoT and ML to create a robust framework that addresses the mobility challenges faced by the elderly population. IoT devices, such as wearable sensors and smart home systems, continuously monitor the physical activities and environmental conditions, providing real-time data. Machine learning algorithms process this data to predict potential mobility issues, personalize mobility plans, and even alert caregivers in case of emergencies [I, II]. The application of ML in analyzing gait patterns, detecting falls, and assessing the risk of mobility-related injuries demonstrates significant promise. Personalized mobility solutions can be developed through continuous learning and adaptation to an individual’s changing health status and mobility needs [I]. Additionally, this integration fosters independence among the elderly, reducing the reliance on caregivers and healthcare systems. The study further discusses the implementation challenges, including data privacy concerns, the need for user-friendly interfaces, and the integration of heterogeneous IoT devices. Through case studies and pilot projects, the paper illustrates successful deployments of IoT and ML technologies in real-world settings, highlighting the improvements in mobility and overall well-being for the elderly [III].In conclusion, the synergistic use of IoT and ML offers a transformative approach to support personal mobility in the elderly, paving the way for safer, more autonomous living environments [I, IV]. Future research directions include enhancing algorithm accuracy, expanding sensor capabilities, and ensuring robust data security measures to fully realize the potential of these technologies in elder care.