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1. Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning

   Shahnavaz, Farid; Tavassoli, Riley; Akhavian, Reza (January 2023, 2023 ASCE International Conference on Computing in Civil Engineering)

   Turkan, Yelda; Louis, Joseph; Leite, Fernanda; Ergan, Semiha (Ed.)

   Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers’ activities. HAR has many applications in human-robot interaction research to enable robots’ understanding of human counterparts’ activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction. 

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2. w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

   https://doi.org/10.3390/s20185356  

   Bhat, Ganapati; Tran, Nicholas; Shill, Holly; Ogras, Umit Y. (September 2020, Sensors)

   null (Ed.)

   Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%. 

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3. Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks

   https://doi.org/10.1109/IROS40897.2019.8968615  

   Frank, Andrea E.; Kubota, Alyssa; Riek, Laurel D. (January 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   In this work, we present a novel non-visual HAR system that achieves state-of-the-art performance on realistic SCE tasks via a single wearable sensor. We leverage surface electromyography and inertial data from a low-profile wearable sensor to attain performant robot perception while remaining unobtrusive and user-friendly. By capturing both convolutional and temporal features with a hybrid CNN-LSTM classifier, our system is able to robustly and effectively classify complex, full-body 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 F1-scores up to 84% over 31 strenuous activity classes. To our knowledge, we are the first to robustly identify complex full-body tasks using a single, unobtrusive sensor feasible for real-world use in SCEs. Using our approach, robots will be able to more reliably understand human activity, enabling them to safely navigate sensitive, crowded spaces. 

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4. SensorGAN: A Novel Data Recovery Approach for Wearable Human Activity Recognition

   https://doi.org/10.1145/3609425  

   Hussein, Dina; Bhat, Ganapati (May 2024, ACM Transactions on Embedded Computing Systems)

   Human activity recognition (HAR) and, more broadly, activities of daily life recognition using wearable devices have the potential to transform a number of applications, including mobile healthcare, smart homes, and fitness monitoring. Recent approaches for HAR use multiple sensors on various locations on the body to achieve higher accuracy for complex activities. While multiple sensors increase the accuracy, they are also susceptible to reliability issues when one or more sensors are unable to provide data to the application due to sensor malfunction, user error, or energy limitations. Training multiple activity classifiers that use a subset of sensors is not desirable, since it may lead to reduced accuracy for applications. To handle these limitations, we propose a novel generative approach that recovers the missing data of sensors using data available from other sensors. The recovered data are then used to seamlessly classify activities. Experiments using three publicly available activity datasets show that with data missing from one sensor, the proposed approach achieves accuracy that is within 10% of the accuracy with no missing data. Moreover, implementation on a wearable device prototype shows that the proposed approach takes about 1.5 ms for recovering data in the w-HAR dataset, which results in an energy consumption of 606 μJ. The low-energy consumption ensures that SensorGAN is suitable for effectively recovering data in tinyML applications on energy-constrained devices. 

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5. AugToAct: scaling complex human activity recognition with few labels

   https://doi.org/10.1145/3360774.3360831  

   Faridee, Abu Zaher; Khan, Md Abdullah; Pathak, Nilavra; Roy, Nirmalya (November 2019, 16th ACM/EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), 2019)

   Human activity recognition (HAR) from wearable sensor data has recently gained widespread adoption in a number of fields. However, recognizing complex human activities, postural and rhythmic body movements (e.g., dance, sports) is challenging due to the lack of domain-specific labeling information, the perpetual variability in human movement kinematics profiles due to age, sex, dexterity and the level of professional training. In this paper, we propose a deep activity recognition model to work with limited labeled data, both for simple and complex human activities. To mitigate the intra- and inter-user spatio-temporal variability of movements, we posit novel data augmentation and domain normalization techniques. We depict a semi-supervised technique that learns noise and transformation invariant feature representation from sparsely labeled data to accommodate intra-personal and inter-user variations of human movement kinematics. We also postulate a transfer learning approach to learn domain invariant feature representations by minimizing the feature distribution distance between the source and target domains. We showcase the improved performance of our proposed framework, AugToAct, using a public HAR dataset. We also design our own data collection, annotation and experimental setup on complex dance activity recognition steps and kinematics movements where we achieved higher performance metrics with limited label data compared to simple activity recognition tasks. 

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