Abstract—Obesity prevention and treatment as well as healthy life style recommendation requires the estimation of everyday physical activity. Monitoring posture allocations and activities with sensor systems is an effective method to achieve the goal. However, at present, most devices available rely on multiple sensors distributed on the body, which might be too obtrusive for everyday use. In this study, data was collected from a wearable shoe sensor system (SmartShoe) and a decision tree algorithm was applied for classification with high computational accuracy. The dataset was collected from 9 individual subjects performing 6 different activities—sitting, standing, walking, cycling, and stairs ascent/descent. Statistical features were calculated and the classification with decision tree classifier was performed, after which, advanced boosting algorithm was applied. The computational accuracy is as high as 98.85% without boosting, and 98.90% after boosting. Additionally, the simple tree structure provides a direct approach to simplify the feature set. I. INTRODUCTION The World Health Organization (WHO) predicts that overweight and obesity may soon become the most significant cause of poor health replacing more traditional public health concerns, such as under-nutrition and infectious diseases [1]. Obesity may have a significant effect on health, leading to reduced life expectancy and increased health problems including heart disease, type 2 diabetes, obstructive sleep apnea, osteoarthritis and certain types of cancers [2]. In addition to these health impacts, obesity may cause many social stigmatization problems. Obesity is due to a sustained positive energy balance and is typically coupled with low level of physical activity [4] [5]. In other words, obesity may be caused by excessive food energy intake and lack of physical activity. A sedentary lifestyle plays a significant role in obesity. The amount of work that is not physically demanding is increasing worldwide. Moreover, there appear to be declines in levels of physical activity in walking due to mechanized transportation, and declines in energy expenditure in housework due to laborsaving technology at home. Studies show that there is an association between television viewing time and the risk of obesity [6]. Therefore, an accurate monitoring of physical activity directly helps in the research of obesity. Monitoring of everyday life activities may also provide detailed recommendations to people who are seeking a healthy lifestyle. Ting Zhang and Wenlong Tang are with Department of Electrical and Computer Engineering, University of Alabama. Edward S. Sazonov is with Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487 USA (Phone: 205-348-1981; Fax: 205-348-6959; e-mail: esazonov@claws,eng.ua.edu) For monitoring physical activities and allocations of human beings, various devices and systems were proposed by different research groups. For example, Bao and Intille [7] mounted accelerometers on the wrist, upper arm, hip, ankle and thigh, with the evaluation of a single best sensor location. They achieved an accuracy of 84% in the activity recognition for 20 different activities. Pirttikangas et al. [8] attached accelerometers to left and right wrists, right thigh and a necklace, with a recognition accuracy of 93% for 17 different activities. However, those devices rely on sensors distributed on the body might be too obtrusive for everyday use. To develop the systems to be more convenient for real-life usage, Zhang et al. [9] proposed a single tri-axial accelerometer placed on the waist and achieved a classification accuracy of 80%. Long et al. [10] proposed a 3D-accelerometer in a smart phone and achieved a recognition accuracy of 82.8%. A wearable non-obtrusive device to reach high classification accuracy in detecting posture activities still remains a desire and challenge. Various algorithms have been applied in physics activity classification, such as Support Vector Machines [11] [12], Artificial Neural Network (ANN) [13], Hidden Markov Model (HMM) [14], Continuous Activity Recognition (CAR) algorithm [10]. Researchers are still seeking for an optimal solution that combines a computational effective algorithm and an advanced sensor system. In this study, data was acquired from a wearable shoe sensor system (SmartShoe) developed previously by our group [15]. After statistical feature computation from sensor signals, decision tree algorithm with boosting [17] was used for classification. This approach reached high classification accuracy. The simple tree structure provided a direct approach to simplify the feature set and this can help improving computational efficiency. II. METHODS A. Wearable shoe sensors and data collection The sensor system embedded into the shoes contains sensors to collect plantar pressure data and heel acceleration data. For each shoe, there are five force-sensitive registers integrated in a flexible insole, positioned under heel, heads of metatarsal bones, and the hallux. With this configuration, differentiation of the most critical parts of the gait cycle, such as heel strike, stance phase and toe-off can be performed. The motion information is provided by a 3-D accelerometer positioned on the back of each shoe. This wireless sensor system is lightweight, minimally obtrusive and with advanced power saving strategies [15]. Classification of Posture and Activities by Using Decision Trees Ting Zhang, Wenlong Tang, Member, IEEE and Edward S. Sazonov, Senior Member, IEEE 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012 4353 978-1-4577-1787-1/12/$26.00 ©2012 IEEE