Activity Recognition from Accelerometer Data Based on Supervised Learning for Wireless Sensor Network

The number of elderly people is increasing day by day. Elderly people are physically very weak. Most of them have to consult doctors regularly. Wireless Sensor Network is designed to keep incessant observations on many elderly people at a time. This WSN is comprised of many sensor nodes. For the recognition of elderly activities, accelerometer sensors are broadly used in WSN since they are available on almost every phone. Recognition of elderly activities is being widely researched and it has great importance in the medical field. An elderly is very likely to be a victim of a sudden accident. It is an urgent need to detect that accident so that immediate treatment can be ensured. In this paper, we have mentioned some important features in the time, frequency, and time-frequency domain which can be used as a combination to recognize activities from accelerometer data more accurately. We have extracted those efficient features from the HASC dataset to perform activity recognition by exploiting Support Vector Machine (SVM). We have obtained 97.05% accuracy on HASC dataset. Considering the challenges involved in recognizing human activities from accelerometer data, the result is quite satisfactory