Cross-Body Transfer Learning for Human Activity Recognition

Human activity recognition (HAR) is the field of using analysis of sensor recordings to detect and determine what activity a person is doing. At the lowest level, it detects simple movements like standing, sitting, walking, and others. These movements are key components for measuring a person’s health and are the subject of many clinical assessments. Devices like hearing aids are the perfect target for HAR from head-worn sensors as many older adults wear and use them regularly. A major constraint for head-worn human-activity-recognition (HAR) deep learning models is gathering enough data. There are many HAR accelerometer datasets recorded from other parts of the body which may fill this gap. In this paper, we show that we can use cross-body transfer learning to improve HAR classification on the UCA-E-HAR head dataset which was recorded using smart glasses. To show this, we gathered datasets from the ankle, thigh, lower back, chest, wrist, and pocket to be used for transfer learning. The datasets included are KU-HAR, Forth, GOTOV, HARTH, Motionsense, Har70+, and UCA-E-HAR. We found that the classification using Resnetv1-6 model for the head data performance improved by 8% to 84.8% for transfer learning models pretrained on all available data from the other body locations. and the models were also better at differentiating between similar tasks such as standing and sitting. We then showed that removing standing from the possible movements, due to its similarity to sitting, allowed the base model performance to increase from 76.8% to 90.8% for a model trained using only head data. The transfer learning model trained on all available data improved performance to 94.5%. This shows that there are benefits for performing cross-body transfer learning for HAR classification.