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Found 6 papers, 1 papers with code
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition
While Human Activity Recognition (HAR) systems have been widely employed in recognizing the activities of individuals, existing systems focus on the duration of macro activities (i. e. a sequence of repetitions of the same exercise), neglecting the ability to discern micro activities (i. e. the individual repetitions of the exercises), in the case of OEP.
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network
A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments.
Eating Speed Measurement Using Wrist-Worn IMU Sensors in Free-Living Environments
Experimental results shows that the proposed approach achieves a mean absolute percentage error (MAPE) of 0. 110 and 0. 146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement.
A Multi-Stage Temporal Convolutional Network for Volleyball Jumps Classification Using a Waist-Mounted IMU
In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps.
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
The results showed the potential of monitoring the compliance of OEP using a single IMU in daily life.
Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention
The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.
