Abstract. The proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of Things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. The gesture recognition model was built using the data collected from 52 unique subjects. The model was mapped onto Intel ® Quark TM SE Pattern Matching Engine, and fieldtested using 8 additional subjects achieving 92% performance.

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 TM SE Pattern Matching Engine, and fieldtested using eight additional subjects achieving performance up to 91%.