Algorithm development and implementation of activity recognition system utilizing wearable MEMS sensors
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Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Medical and gerontology literature often associates lack of physical activity with fall. Therefore, an autonomous activity recognition system can help elderly people and their care givers to track their level of activities performed in day-to-day life. Moreover, activity recognition is also required in other applications such as medical monitoring and post-fall/injury rehabilitation. Though many researchers have shown the utility of several different sensors or sensor networks for achieving activity recognition, MEMS-based sensors are leading the race because of the advantages they have in terms of cost, form-factor, and being easily made into mobile units. Previously developed activity recognition systems utilizing MEMS-based tri-axial accelerometers have provided mixed results, with subject-to- subject variability. This work presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The system was developed in a fashion such that user-comfort and accuracy is maximized, while reducing the level of user training. The goal is not only to help the system attain high accuracy, but also to achieve high user-acceptance such that the system is practically implementable. Different test methodologies were also investigated and implemented so as to estimate errors effectively in a relatively small set of samples. The algorithm presented in this work utilizes data from a single, waist- mounted tri-axial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby, reducing user training. Activity recognition results on seven subjects with leave-one-person-out error estimates showed overall accuracy of about 98%. Accuracy for each of the individual activity was also more than 95%. Error estimates calculated using Bootstrapping methodology also confirmed high accuracy for the system.