Design considerations for the WISDM smart phone-based sensor mining architecture

Abstract

Smart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), direction sensor (compass), audio sensor (microphone), image sensor (camera), proximity sensor, light sensor, and temperature sensor. Combined with the ubiquity and portability of these devices, these sensors provide us with an unprecedented view into people's lives-and an excellent opportunity for data mining. But there are obstacles to sensor mining applications, due to the severe resource limitations (e.g., power, memory, bandwidth) faced by mobile devices. In this paper we discuss these limitations, their impact, and propose a solution based on our WISDM (WIireless Sensor Data Mining) smart phone-based sensor mining architecture.

Identifying user traits by mining smart phone accelerometer data

“…We address this topic in some detail in a recent paper describing our WISDM sensor mining architecture and platform [12]. But there also needs to be some education of the public about the benefits and dangers of this technology, since no application can be made perfectly secure and no data can always be guaranteed to remain private.…”

“…Finally, in Section 3.4 we describe the prediction algorithms and the methodology used to build and evaluate the predictive models. The data transformation step described in Section 3.2 is essentially identical to the one used in our prior work [10][11][12] but the other sections are all new.…”

“…That work used the same WISDM platform [12,21] that is used in this paper and also was based on accelerometer data from Android smart phones. The main difference is that in the prior work the goal was to identify users whereas the goal here is to learn about their traits.…”

“…The applications are built on the hardware and software platform that we are developing [12]. Currently, this platform relies on Android-based smart phones, which were used for all of the experiments described in this paper.…”

PRO-Fit: A personalized fitness assistant framework

“…We experimentally evaluated various classification algorithms to identify the most appropriate ones for this task. For our experiments, we used the dataset collected by Kwapisz et al [11], publicly available through the WISDM lab [14], including activity data collected from android devices. The data was collected at an interval of 50ms, which means it contains 20 samples per second.…”