@article{da silva_zhong_chen_lobaton_2022, title={Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks}, volume={13}, ISSN={["2078-2489"]}, url={https://doi.org/10.3390/info13070338}, DOI={10.3390/info13070338}, abstractNote={Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.}, number={7}, journal={Information}, publisher={MDPI AG}, author={da Silva, Rafael Luiz and Zhong, Boxuan and Chen, Yuhan and Lobaton, Edgar}, year={2022}, month={Jul}, pages={338} }
 @article{zhong_silva_li_huang_lobaton_2021, title={Environmental Context Prediction for Lower Limb Prostheses With Uncertainty Quantification}, volume={18}, ISSN={["1558-3783"]}, url={https://doi.org/10.1109/TASE.2020.2993399}, DOI={10.1109/TASE.2020.2993399}, abstractNote={Reliable environmental context prediction is critical for wearable robots (e.g., prostheses and exoskeletons) to assist terrain-adaptive locomotion. This article proposed a novel vision-based context prediction framework for lower limb prostheses to simultaneously predict human’s environmental context for multiple forecast windows. By leveraging the Bayesian neural networks (BNNs), our framework can quantify the uncertainty caused by different factors (e.g., observation noise, and insufficient or biased training) and produce a calibrated predicted probability for online decision-making. We compared two wearable camera locations (a pair of glasses and a lower limb device), independently and conjointly. We utilized the calibrated predicted probability for online decision-making and fusion. We demonstrated how to interpret deep neural networks with uncertainty measures and how to improve the algorithms based on the uncertainty analysis. The inference time of our framework on a portable embedded system was less than 80 ms/frame. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. Note to Practitioners—This article was motivated by two practical problems in computer vision for wearable robots: First, the performance of deep neural networks is challenged by real-life disturbances. However, reliable confidence estimation is usually unavailable and the factors causing failures are hard to identify. Second, evaluating wearable robots by intuitive trial and error is expensive due to the need for human experiments. Our framework produces a calibrated predicted probability as well as three uncertainty measures. The calibrated probability makes it easy to customize prediction decision criteria by considering how much the corresponding application can tolerate error. This study demonstrated a practical procedure to interpret and improve the performance of deep neural networks with uncertainty quantification. We anticipate that our methodology could be extended to other applications as a general scientific and efficient procedure of evaluating and improving intelligent systems.}, number={2}, journal={IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhong, Boxuan and Silva, Rafael Luiz and Li, Minhan and Huang, He and Lobaton, Edgar}, year={2021}, month={Apr}, pages={458–470} }