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Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision.
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Introducing a new benchmarked dataset for activity monitoring
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Reallocating time from machine-learned sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk
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UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor
Chen, C., Jafari, R. & Kehtarnavaz, N · 2015
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Chen, C., Jafari, R. & Kehtarnavaz, N · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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On-body localization of wearable devices: An investigation of position-aware activity recognition
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Testing self-report time-use diaries against objective instruments in real time
Gershuny, J. et al · 2020
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Masked reconstruction based self-supervision for human activity recognition
Haresamudram, H. et al · 2020
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Are accelerometers for activity recognition a dead-end?
Tong, C., Tailor, S. A. & Lane, N. D · 2020
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Opportunity++: A Multimodal Dataset for Video- and Wearable, Object and Ambient Sensors-Based Human Activity Recognition
Ciliberto, M., Fortes Rey, V., Calatroni, A., Lukowicz, P. & Roggen, D · 2021
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Validation of wearable camera still images to assess posture in free-living conditions
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Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
Walmsley, R. et al · 2021
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Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis
Creagh, A. P. et al · 2022
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Association of step counts over time with the risk of chronic disease in the all of us research program
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Assessing the state of self-supervised human activity recognition using wearables
Haresamudram, H., Essa, I. & Plötz, T · 2022
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Collossl: Collaborative self-supervised learning for human activity recognition
Jain, Y., Tang, C. I., Min, C., Kawsar, F. & Mathur, A · 2022
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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Yuan, H. et al · 2022
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Wearable movement-tracking data identify parkinson’s disease years before clinical diagnosis
Schalkamp, A.-K., Peall, K. J., Harrison, N. A. & Sandor, C · 2023
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At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis
Gupta, A. S., Patel, S., Premasiri, A. & Vieira, F · 2023
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Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank
Small, S. R. et al · 2023
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Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors
Hoelzemann, A., Romero, J. L., Bock, M., Laerhoven, K. V. & Lv, Q · 2023
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WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition, 10.48550/ARXIV.2304.05088 (2023)
Bock, M., Kuehne, H., Van Laerhoven, K. & Moeller, M · 2023
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Topological Nonlinear Analysis of Dynamical Systems in Wearable Sensor-Based Human Physical Activity Inference
Yan, Y. et al · 2023
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Device-measured movement behaviours in over 20,000 china kadoorie biobank participants
Chen, Y. et al · 2023
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Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality
Yuan, H. et al · 2023
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