Investigating an IoT-Integrated Cane System for Accurate Gait Analysis and Fall Detection

Thailand has a population of 67 million, and the country has entered an aging society. According to the World Health Organization (WHO) statistics, 10% of the population is elderly (over 60 years old). For this reason, the IoT-Integrated Cane System is researched and implemented to enhance elderly mobility and safety. This research investigates the potential of an IoT-based cane system integrated with the GY-521 module and NB-IoT wireless communication device for gait analysis and fall detection among the elderly. The proposed smart IoT cane brings the concept of a 3-axis accelerometer with a range of -10 to +10 g average. The three orthogonal parts of the acceleration caused by gravity are measured. Namely, By collecting and analyzing sensor data in diverse scenarios - walking on flat, stair, and sloped surfaces and during a walk -fall -walk test - the study evaluates the sensor's ability to discern movement patterns. Statistical analysis, including Coefficient of Variation (CV) calculation, validates the sensor's accuracy in detecting and differentiating various scenarios. Visual representations enhance our understanding of the sensor's responses. The outcomes endorse its utility in gait analysis, fall detection, and activity recognition, yielding insights for improving the safety and well-being of the elderly.