Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201

On the Benefit of Generative Foundation Models for Human Activity Recognition

In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI, including Large Language Models (LLMs) and motion synthesis models, we believe that generative AI can address this data scarcity by autonomously generating virtual IMU data from text descriptions. Beyond this, we spotlight several promising research pathways that could benefit from generative AI for the community, including the generating benchmark datasets, the development of foundational models specific to HAR, the exploration of hierarchical structures within HAR, breaking down complex activities, and applications in health sensing and activity summarization.Comment: Generative AI for Pervasive Computing (GenAI4PC) Symposium within UbiComp/ISWC 202

Classification of trunk motion based on inertial sensors

55 páginasPor mucho tiempo en la ingeniería se ha trabajado para la valoración y prevención de desórdenes musculo esqueléticos de la espalda. El uso de sistemas de captura de movimiento es una de las técnicas más utilizadas en este ámbito, considerada el estándar, es un sistema de alto costo y limitaciones considerables. El uso de sensores de inercia en los últimos años ha llegado a ser muchos más frecuente gracias a los avances tecnológicos que lo han llevado a ser más accesible y compacto. Este proyecto se basa en el uso de sensores de inercia para la clasificación de movimientos de la espalda, lo cual ayuda a la valoración y prevención de desórdenes musculo esqueléticos. En el presente texto, se presenta todo el proceso de desarrollo de un sistema para la identificación de movimientos de la espalda, mediante el uso de las señales de los sensores de inercia. Se presentaran experimentos y resultados del uso de un sistema de captura de movimiento comparado con el sistema de sensores de inercia. El programa propuesto para la clasificación de los tres movimientos del tronco (flexión, lateral y rotación) trae consigo importantes ventajas de fiabilidad y libertad espacial. El sistema desarrollado permite la valoración de los movimientos del tronco. La clasificación de estos movimientos utilizando sensores de inercia es un método considerablemente mucho más portable comparado con un sistema de captura de movimiento. También, este sistema se puede usar en diferentes espacios sin mayores esfuerzos y los límites transnacionales de movimientos son mucho más amplios en comparación con sistemas de captura de movimientos. La clasificación permite la adición de nuevas características para la identificación más precisa de movimientos o posturas que permitirán una mejor valoración para la prevención de desórdenes musculo esqueléticos del trono.PregradoIngeniero(a) Biomédico(a

Unsupervised Deep Learning-based clustering for Human Activity Recognition

One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labelled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual information metrics.Comment: 2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin

Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification performance. Assuming that changes in the sensor placement mainly result in shifts in the feature distributions, we propose an unsupervised adaptive classifier that calibrates itself using an online version of expectation-maximisation. Tests using three activity recognition scenarios show that the proposed adaptive algorithm is robust against shift in the feature space due to sensor displacement and rotation. Moreover, since the method estimates the change in the feature distribution, it can also be used to roughly evaluate the reliability of the system during online operatio