Deep learning models have shown excellent performance in human activity recognition tasks. However, these models typically require large amounts of computational resources, which makes them inefficient to deploy on edge devices. Furthermore, the superior performance of deep learning models relies heavily on the availability of large datasets to avoid over-fitting. However, the expensive efforts for labeling limits the amount of datasets. We address both challenges by designing a more lightweight model, called TinyHAR. TinyHAR is designed specifically for human activity recognition employing different saliency of multi modalities, multimodal collaboration, and temporal information extraction. Initial experimental results show that TinyHAR is several times smaller and often meets or even surpasses the performance of DeepConvLSTM, a state-of-the-art human activity recognition model.

Earphones have evolved from pure audio devices to "earables" that are capable of advanced sensing. Bespoke research devices have shown the unique sensing capabilities of the earable platform; however, they are hard to replicate and require expertise to develop in the first place. In this paper, we present OpenEarable 2.0 - an open source, unified platform that integrates a larger number of sensors for conducting comprehensive earable research. OpenEarable 2.0 works as regular binaural Bluetooth earphones and features two ultrasound capable microphones (inward/outward), a 3-axis ear canal accelerometer/bone microphone, a 9-axis head inertial measurement unit, pulse oximeter, optical temperature sensor, ear canal pressure sensor, and microSD card. These capabilities allow for the detection and measurement of 30+ phenomena on the ear that can be used across a wide range of applications in health monitoring, activity tracking, human-computer-interaction and authentication. We describe the design and development of OpenEarable 2.0 which follows best open hardware practices and achieves commercial-level wearability. We provide justification for the selection and placement of integrated sensors and include in-depth descriptions of the extensible, open source firmware and hardware that are implemented using free to use tools and frameworks. For real-time sensor control and data recording we also contribute a web-based dashboard and mobile smartphone app. The wearability and ability to sense different phenomena are validated in four studies which showcases how OpenEarable 2.0 provides accurate measurements in comparison to established gold-standard measurements. We further demonstrate that OpenEarable 2.0 can be assembled by inexperienced users, and that undergraduate students can build applications using the OpenEarable platform.

Sensor-based HAR models face challenges in cross-subject generalization due to the complexities of data collection and annotation, impacting the size and representativeness of datasets. While data augmentation has been successfully employed in domains like natural language and image processing, its application in HAR remains underexplored. This study presents AutoAugHAR, an innovative two-stage gradient-based data augmentation optimization framework. AutoAugHAR is designed to take into account the unique attributes of candidate augmentation operations and the unique nature and challenges of HAR tasks. Notably, it optimizes the augmentation pipeline during HAR model training without substantially extending the training duration. In evaluations on eight inertial-measurement-units-based benchmark datasets using five HAR models, AutoAugHAR has demonstrated superior robustness and effectiveness compared to other leading data augmentation frameworks. A salient feature of AutoAugHAR is its model-agnostic design, allowing for its seamless integration with any HAR model without the need for structural modifications. Furthermore, we also demonstrate the generalizability and flexible extensibility of AutoAugHAR on four datasets from other adjacent domains. We strongly recommend its integration as a standard protocol in HAR model training and will release it as an open-source tool1.