Daniel Olguin Olguin, Alex 'Sandy' Pentland

Abstract

In recent years much work has been done on human activity recognition using wearable sensors. As we begin to deploy hundreds and even thousands of wearable sensors on regular workers, hospital patients, and army soldiers, the question shifts more toward a balance between what information can be gained and their broad immediate user acceptance. In this paper we compare the activity classification accuracy of four different configurations of accelerometer placement on the human body using hidden Markov models (HMMs). We find the classification accuracy of a single accelerometer placed in three different parts of the body and evaluate whether there is a signifi- cant improvement in recognition accuracy by adding multiple accelerometers or not. We also find the number of hidden states that best models each activity by achieving the lowest test error using K-fold cross-validation.