This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13 to 22 percent of energy, while achieving similar pattern recognition performance and reconstruction error.

This paper presents a motion-adaptive approach to duty-cycling the orientation estimation of a wearable inertial measurement unit (IMU). Specifically, a proportional forward-controller was employed to dynamically tune the sampling and orientation update rate of a Madgwick filter. An energy model was defined to estimate the power consumed by individual inertial sensors and processing elements. We demonstrate the efficacy of our controller by analysing multi-day free-living motion recordings of wrist-worn IMUs. In a comparison of the orientation estimation between the full duty-cycle and the adaptive one, average error was approx. 10 degrees while saving more than 30% of sensor node energy. To assess the orientation information retained by our approach, we analysed the binary pattern classification performance for recognising food and fluid intake gestures. Recognition performance of the motion-adaptive duty-cycling remained above 90% up to an energy saving of approx. 32.5%, confirming the profound potential of the method.

We present a spotting network composed of Gaussian Mixture Hidden Markov Models (GMM-HMMs) to detect sparse natural gestures in free living. The key technical features of our approach are (1) a method to mine non-gesture patterns that deals with the arbitrary data (Null Class), and (2) an optimisation based on multipopulation genetic programming to approximate spotting network's parameters across target and non-target models. We evaluate our GMM-HMMs spotting network in a novel free living dataset, including totally 35 days of annotated inertial sensor's recordings from seven participants. Drinking was chosen as target gesture. Our method reached an average F1-score of over 74% and clearly outperformed an HMM-based threshold model approach. The results suggest that our spotting network approach is viable for sparse natural pattern spotting.