Unsupervised learning for human activity recognition using smartphone sensors
We investigate activity recognition using unsupervised learning, with a smartphone. The number of activities can be determined by the Cali?skiHarabasz index. The mixture of Gaussian outperforms when the number of activities is known. The hierarchical clustering and DBSCAN attain above 90% accuracy for appropriate settings. The study provides an idea for activity recognition methods without training datasets.
unsupervised learning human activity recognition smartphone sensors healthcare services sensor data analysis