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

ABSTRACT 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%.

0 Bookmarks
 · 
226 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.
    Sensors (Basel, Switzerland). 01/2014; 14(6):10146-10176.
  • Source
    [Show abstract] [Hide abstract]
    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
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.
    KIPS Transactions on Software and Data Engineering. 01/2013; 2(1).

Full-text

Download
2 Downloads