Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement

Mathie, M.; Coster, Adelle C.F.; Lovell, Nigel H.; Celler, Branko G.

doi:10.1088/0967-3334/25/2/r01

Cited by 690 publications

(447 citation statements)

References 100 publications

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“…Previous research initiatives have relied on self-reporting methods to acquire information about activities that correspond to sitting routines. On the other hand, accelerometers can provide recognition of physical activities such as sitting, running, standing [11,12] and in addition record further details such as duration, frequency and intensity of movements. Accelerometers have become critical in investigation of sedentary habits that are difficult to recall with the questionnaire method.…”

Section: Sedentary Work Stylementioning

confidence: 99%

“…Accelerometers provide an important research tool able to reliably measure and classify a number of physical activities, including walking, jogging, sitting, standing [12], and more complex activities such as estimation of metabolic energy expenditure, sit-to stand transfers, and assessment of balance and intensity of physical activity [11]. In our study we focused on distinguishing between sedentary and non-sedentary time.…”

Section: Non-sedentary Timementioning

confidence: 99%

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Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes

Matic

Osmani

Popleteev

et al. 2011

Modeling and Using Context

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Abstract. Study of mood and in turn mood changes is an important index of general wellbeing state and can also be an indicator of various mood disorders including clinical depression and bipolar disorder. While there have been clinical studies of mood, less emphasis has been placed on the factors that affect the mood, especially in workplace. The typical approach taken by these studies is to use clinical questionnaires in order to record the activities that impact the mood. However, recording activities that may have an influence on the mood in this manner suffers from a number of issues including floor effect and difficulty in recalling past activities. Our work instead has focused on using unobtrusive monitoring technology to study mood changes during office hours and a set of associated factors that influence these changes. We have primarily studied two factors and their impact on the mood changes, namely social activity and nonsedentary work style. The pilot study ran over the course of 7 days of measurements with the participation of 9 knowledge workers. The results have shown that mood changes are highly correlated with both social interactions and non-sedentary work style. This study is the first to investigate the correlation between mood changes and non-sedentary behavior patterns, opening up a research avenue to explore psychological effects of increasing prevalence of sedentary behavior.

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Section: Sedentary Work Stylementioning

confidence: 99%

Section: Non-sedentary Timementioning

confidence: 99%

Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes

Matic

Osmani

Popleteev

et al. 2011

Modeling and Using Context

View full text Add to dashboard Cite

show abstract

“…This way, the sensors measured the anterior elevation-extension, abduction-adduction and internal-external rotation of the shoulder. The signal from the sensors was amplified and low-pass filtered (cut-off frequency: 17 Hz) to remove noise [12,13]. The sensors and their conditioning electronics were packaged in a very small box (25 mm Â 25 mm Â 13 mm).…”

Section: Subjects and Materialsmentioning

confidence: 99%

Outcome evaluation in shoulder surgery using 3D kinematics sensors

Coley¹,

Jolles²,

Farron³

et al. 2007

Gait & Posture

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A new method of scoring systems for the functional assessment of the shoulder is presented. 3D accelerometers and gyroscopes attached on the humerus were used to differentiate a healthy from a painful shoulder. The method was first tested on 10 healthy volunteer subjects with no shoulder pathology. The system was then tested on 10 patients with unilateral shoulder pathology (rotator cuff disease, osteoarthritis) before and after surgery (3, 6 months). In order to evaluate the system, nine tests based on the Simple Shoulder Test (SST) were performed on each shoulder for each patient. Three scores were defined: the P score was based on the angular velocities and accelerations of the humerus; the RAV score was based only on the angular velocities of the humerus; the M score was based on the sum of all moments of the humerus. Our kinematic scores indicated significant differences between baseline and follow-up ( p < 0.05) and differentiated between patients with varying severity of the same condition. We demonstrated a reliable technique of evaluating shoulder pathology and the results of surgery. #

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“…It promises to have great impact on a variety of domains like elder care [6], fitness monitoring [9,14], and intelligent contextaware applications [5]. Recently several researchers have used the accelerometers embedded in smartphones to detect activities such as walking, standing, running and sitting with the goal of developing context-aware applications [8,10].…”

Section: Related Workmentioning

confidence: 99%

Coarse In-Building Localization with Smartphones

Parnandi

Le²,

Vaghela³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization approach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the user's dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.

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Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement

Cited by 690 publications

References 100 publications

Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes

Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes

Outcome evaluation in shoulder surgery using 3D kinematics sensors

Coarse In-Building Localization with Smartphones

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