Abstract
The proliferation of low power and low cost continuous sensing has generated an immense interest in the area of activity recognition. However, the real time detection is still a challenge for several reasons: requirement from the user to specify the type of activity, complex algorithms, and collection of data from multiple devices. In this paper, we describe a generalized activity recognition system, its applications, and the challenges involved in implementing the algorithm in resource-constrained devices. The distinctive aspects of our study include: 1) automatic detection and recognition of different activities (running, walking, crawling, climbing, and pronating), 2) using just one axis from an accelerometer sensor, and 3) simple features and pattern matching algorithm leading to computationally inexpensive and memory efficient system suitable for resource-constrained wearable devices. The activity recognition model was trained using data collected from 52 unique subjects. The model was mapped onto Intel® Quark™ SE Pattern Matching Engine, and field-tested using eight additional subjects achieving performance up to 91%.