A Heterogeneous Clustering Approach for Human Activity Recognition

Human Activity Recognition (HAR) has a growing research interest due to the widespread presence of motion sensors on user’s personal devices. The performance of HAR system deployed on large-scale is often significantly lower than reported due to the sensor-, device-, and person-specific heterogeneities. In this work, we develop a new approach for clustering such heterogeneous data, represented as a time series, which incorporates different level of heterogeneities in the data within the model. Our method is to represent the heterogeneities as a hierarchy where each level in the hierarchy overcomes a specific heterogeneity (e.g., a sensor-specific heterogeneity). Experimental evaluation on Electromyography (EMG) sensor dataset with heterogeneities shows that our method performs favourably compared to other time series clustering approaches.