Conference Paper

Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations

DOI: 10.1007/978-3-642-16355-5_42 Conference: Ubiquitous Intelligence and Computing - 7th International Conference, UIC 2010, Xi'an, China, October 26-29, 2010. Proceedings
Source: DBLP


This paper uses accelerometer-embedded mobile phones to monitor one’s daily physical activities for sake of changing people’s
sedentary lifestyle. In contrast to the previous work of recognizing user’s physical activities by using a single accelerometer-embedded
device and placing it in a known position or fixed orientation, this paper intends to recognize the physical activities in
the natural setting where the mobile phone’s position and orientation are varying, depending on the position, material and
size of the hosting pocket. By specifying 6 pocket positions, this paper develops a SVM based classifier to recognize 7 common
physical activities. Based on 10-folder cross validation result on a 48.2 hour data set collected from 7 subjects, our solution
outperforms Yang’s solution and SHPF solution by 5~6%. By introducing an orientation insensitive sensor reading dimension,
we boost the overall F-score from 91.5% to 93.1%. With known pocket position, the overall F-score increases to 94.8%.

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    • "Several researchers developed AR approaches that normalize the orientation of the smartphone, resulting in an increased accuracy [8][9]. Sun et al. [10] studied AR when the location of the smartphone varied. They reported only a modest increase in the accuracy when location-specific classifiers were used, probably due to recognizing only ambulatory activities and driving, where the intensity of the phone's movement is correlated with the activity in a relatively straightforward manner. "
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    ABSTRACT: This paper presents a novel method for activity recognition and estimation of human energy expenditure with a smartphone and an optional heart-rate monitor. The method detects the presence of the devices, normalizes the orientation of the phone, detects its location on the body, and uses location-specific models to recognize the activity and estimate the energy expenditure. The normalization of the orientation and the detection of the location significantly improve the accuracy; the estimated energy expenditure is more accurate than that provided by a state-of-the-art dedicated consumer device.
    PerCom 2015, Saint Louis USA; 03/2015
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    • "bag or pocket), the orientation can change -since the smartphone is not fixed on the body -resulting in reduced activity recognition accuracy. The two main approaches reported in literature are: transforming the coordinates system before applying the classification algorithm [16], [19], or using orientation-independent features [17], [20], [22]. Orientationindependent features are calculated summing or squaring the accelerometer signal over the three axis, after removing the static component due to gravity [20]. "
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    ABSTRACT: Activity recognition for human behavior monitoring is an important research topic in the field of mHealth, especially for aspects of physical activity linked to fitness and disease progress, such as walking and walking speed. Sensors embedded into smartphones recently enabled new opportunities for non invasive activity and walking speed inference. In this paper, we propose a data fusion approach to the problem of physical activity recognition and walking speed estimation using smartphones. Our architecture combines different sensors to take into account practical issues arising in realistic settings, such as variability in phone location and orientation. Additionally, we introduce a novel automatic calibration methodology combining accelerometer and GPS data while walking in unconstrained settings, in order to reduce walking speed estimation error at the individual level. The proposed system was validated in 20 participants while performing sedentary, household, ambulatory and sport activities, in both indoor laboratory and outdoor self-paced settings. We show that by combining accelerometer and gyroscope data, smartphone location can be distinguished between the two most commonly used positions (bag and pocket), regardless of phone orientation (97 % f-score). Location-specific activity recognition models can significantly improve activity recognition performance (p = 0.0010 <; α), especially helping in distinguishing activities involving similar motion patterns (91 % f-score overall, improvements between 4% and 11 % for walking and biking activities). Our proposed method to personalize walking speed estimates, by automatically calibrating walking speed estimation models during a short self-paced walk, reduced walking speed estimation error by 8.8% on average (p = 0.0012 <; α).
    Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on; 03/2014
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    • "We collected approximately 1400 minutes of data for analysis and our results indicate that activity detection is 95.4 percent accurate . In comparison, Lin's solution achieved a 83.3 percent accuracy on the same data set [19] "
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    ABSTRACT: A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as car-diovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.
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