Abstract. Our demonstration consists of a working activity and gait recognition system, implemented on a consumer smartphone. The activity recognition feature allows participants to train various activities, such as running, walking, or jumping, into the phone, and the system can then identify when those activities are performed. The gait recognition feature learns particular characteristics of how participants walk, allowing the phone to identify the carrier. We have implemented a system for activity and gait recognition for the Google Android OS c , which runs on smartphones from a variety of manufacturers. During our demonstration, we will allow participants to try out the various features, either demonstrating the phone's ability to recognize a pre-programmed set of activities (running, jogging, jumping, or walking), or the phone's ability to learn the characteristics of a participants' gait, or style of walking, and showing that the phone can later recognize that participant. Activity and gait recognition using wearable sensors are active research topics [see 8, 1, 9, for example]. Unlike previous work, we do not require specialized wearable sensors or careful placement of the phone on the participant's body. Our application works when the phone is simply placed in a trouser pocket. Additionally, the computation requirements for training these systems are prohibitive for real-time demonstrations, and in many cases even the classification requires more computation that can be afforded on a mobile device such as a smartphone. Our system was designed with computational efficiency in mind, and can both learn classifiers and perform classification in real-time on a low-powered mobile device. The demonstration will be interactive, and participants will be asked to perform different activities, with the classification results displayed on a separate monitor for everyone to see. The activity and gait recognition systems are based on an algorithm called geometric template matching (GTM) [5] for comparing segments of time series.
In REF , Frank et al. introduced a system for activity and gait recognition for smartphones.
Activity Recognition With Mobile Phones
ECML/PKDD
Computer Science