Detailed Human Activity Recognition based on Multiple HMM

A wide array of activities is performed by humans, everyday. In healthcare, precocious detection of movement changes in daily activities and their monitoring, are important contributors to assess the patient general well-being. Several previous studies are successful in activity recognition, but few of them provide a meticulous discrimination. Hereby, we created a novel framework specialized in detailed human activities, where signals from four sensors were used: accelerometer, gyroscope, magnetometer and microphone. A new dataset was created, with 10 complex activities, suchlike opening a door, brushing the teeth and typing on the keyboard. The classifier was based on multiple hidden Markov models, one per activity. The developed solution was evaluated in the offline context, where it achieved an accuracy of 84±4.8%. It also showed a solid performance in other performed tests, where it was tested with different detailed activities, and in simulations of real time recognition. This solution can be applied in elderly monitoring to access their well-being and also in the early detection of degenerative diseases.