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Self-Experimentation for Behavior Change: Design and Formative Evaluation of Two Approaches.

, , , , , and . CHI, page 6837-6849. ACM, (2017)

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A Multi-featured Approach for Wearable Sensor-Based Human Activity Recognition., , , , , , and . ICHI, page 423-431. IEEE Computer Society, (2016)Using periodicity intensity to detect long term behaviour change., , , and . UbiComp/ISWC Adjunct, page 1069-1074. ACM, (2015)Comparing Gaussian Mixture Model and Hidden Markov Model to Classify Unique Physical Activities from Accelerometer Sensor Data., , , , and . ICMLA, page 339-346. IEEE Computer Society, (2016)A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer., , , , and . EMBC, page 2631-2635. IEEE, (2016)Temporal Alignment Improves Feature Quality: An Experiment on Activity Recognition With Accelerometer Data., , , , , and . CVPR Workshops, page 349-357. IEEE Computer Society, (2018)Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers., , , , , , and . Sensors, 18 (11): 3893 (2018)Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data., , , , , and . CoRR, (2022)Learning approach for classification of GENEActiv accelerometer data for unique activity identification., , , and . BSN, page 359-364. IEEE, (2016)Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention., , , , , , , , and . Journal of Biomedical Informatics, (2018)Self-Experimentation for Behavior Change: Design and Formative Evaluation of Two Approaches., , , , , and . CHI, page 6837-6849. ACM, (2017)