Wearable activity recognition for robust humanrobot teaming in safetycritical environments via hybrid neural networks In this work, we present a novel nonvisual HAR system that achieves stateoftheart performance on realistic SCE tasks via a single wearable sensor. We leverage surface electromyography and inertial data from a lowprofile wearable sensor to attain performant robot perception while remaining unobtrusive and userfriendly. By capturing both convolutional and temporal features with a hybrid CNNLSTM classifier, our system is able to robustly and effectively classify complex, fullbody human activities with only this single sensor. We perform a rigorous analysis of our method on two datasets representative of SCE tasks, and compare performance with several prominent HAR algorithms. Results show our system substantially outperforms rival algorithms in identifying complex human tasks from minimal sensing hardware, achieving F1scores up to 84% over 31 strenuous activity classes. To our knowledge, we are the first to robustly identify complex fullbody tasks using a single, unobtrusive sensor feasible for realworld use in SCEs. Using our approach, robots will be able to more reliably understand human activity, enabling them to safely navigate sensitive, crowded spaces.

Smart Phone Based Human Activity Recognition Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the stateoftheart approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for carstep out (CSO), carstep in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.