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A bag-of-words approach for assessing activities of daily living using wrist accelerometer data., , , , , , and . BIBM, page 678-685. IEEE Computer Society, (2017)Power-efficient real-time approach to non-wear time detection for smartwatches., , , , , , and . BHI, page 217-220. IEEE, (2017)Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning., , , , , and . AMIA, AMIA, (2020)Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?, , , , , and . Sensors, 22 (8): 3061 (2022)Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning., , , , , and . Sensors, 21 (10): 3352 (2021)A smartwatch-based framework for real-time and online assessment and mobility monitoring., , , , , , , , and . J. Biomed. Informatics, (2019)Wrist accelerometer shape feature derivation methods for assessing activities of daily living., , , , , and . BMC Medical Informatics Decis. Mak., 18 (S-4): 124:1-124:13 (2018)Adaptive walk detection algorithm using activity counts., , , , , and . BHI, page 161-164. IEEE, (2017)ROAMM: A customizable and interactive smartwatch platform for patient-generated health data., , , , , , , , , and 4 other author(s). IC3, page 150-158. ACM, (2021)Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults., , , , , , , , and . AMIA, AMIA, (2022)