StrengthSense: A Dataset of IMU Signals Capturing Everyday Strength-Demanding Activities
This dataset addresses a gap for researchers and developers in human activity recognition, fitness, and health monitoring, but it is incremental as it primarily provides new data rather than novel methods.
The authors tackled the lack of comprehensive datasets for strength-demanding activities by introducing StrengthSense, an open dataset of IMU signals from 29 subjects capturing 11 strength-demanding and 2 non-strength activities, with validation showing accurate joint angle estimates compared to video data.
Tracking strength-demanding activities with wearable sensors like IMUs is crucial for monitoring muscular strength, endurance, and power. However, there is a lack of comprehensive datasets capturing these activities. To fill this gap, we introduce \textit{StrengthSense}, an open dataset that encompasses IMU signals capturing 11 strength-demanding activities, such as sit-to-stand, climbing stairs, and mopping. For comparative purposes, the dataset also includes 2 non-strength demanding activities. The dataset was collected from 29 healthy subjects utilizing 10 IMUs placed on limbs and the torso, and was annotated using video recordings as references. This paper provides a comprehensive overview of the data collection, pre-processing, and technical validation. We conducted a comparative analysis between the joint angles estimated by IMUs and those directly extracted from video to verify the accuracy and reliability of the sensor data. Researchers and developers can utilize \textit{StrengthSense} to advance the development of human activity recognition algorithms, create fitness and health monitoring applications, and more.