Activity recognition using cell phone accelerometers

Abstract

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10-second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively-just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise.

Activity Recognition for Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models

“…The latest generation of smart phones contain tri-axial accelerometers that measure acceleration along the x, y and z axes ( Figure 1). The ability to detect orientation of the device provides useful information for activity recognition (Ravi, Dandekar, Mysore, & Littman, 2005;Kwapisz, Weiss, & Moore, 2011). In previous work on activity recognition, Ravi et al (2005) attempted to recognize human activities using accelerometer data.…”

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

“…Artificial intelligence, machine learning and recently deep learning approaches have been used in studies showing effective results in identifying different activities from body-fixed sensor data [4], [9], [18]- [20]. A wide variety of approaches has been used to extract the features form the accelerometer data directly using time-varying acceleration signal [4] and frequency analysis [15], [21], [22]. Wavelet analysis is also used to derive the features [18].…”

Ascending and Descending Walking Recognition using Smartphone

“…With the same objective, a system was conducted by numerical experiments on identifying the physical activities of a user [4]. The data was collected from 29 users as they performed daily activities.…”

“…This advantage decreases the cost of dimensional curse in comparison with other researches. For example, the research in [4] needs 43 features for activity classification but the result is quite small for descending stairs (44.3 %) and ascending stairs (61.5 %), which leads to the consideration of combining the two activities as one action. In another research, the classification precisions of walking downstairs and walking upstairs are 87.2% and 72.6% respectively [3].…”

“…Another good point of this research is an activity could be recognized in a short interval of time such as 2 or 4 seconds. The research [4] applies an approximate window size of 2.56 sec, while the study [3] divides data in 10 seconds window segments. A short window time allows the flexible in recognition because sometimes the subjects may perform an activity less than the required duration for recognition.…”