A ModelBased Human Activity Recognition for Humanxe2x80x93Robot Collaboration Human activity recognition is a crucial ingredient in safe and efficient humanxe2x80x93robot collaboration. In this paper, we present a new modelbased human activity recognition system called logical activity recognition system (LCARS). LCARS requires much less training data compared to learningbased works. Compared to other modelbased works, LCARS requires minimal domainspecific modeling effort from users. The minimal modeling is for two reasons: i) we provide a systematic and intuitive way to encode domain knowledge for LCARS and ii) LCARS automatically constructs a probabilistic estimation model from the domain knowledge. Requiring minimal training data and modeling effort allows LCARS to be easily applicable to various scenarios. We verify this through simulations and experiments.

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.