Determining activity using a triaxial accelerometer

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

doi:10.1109/iembs.2002.1053385

Cited by 41 publications

(27 citation statements)

References 4 publications

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“…Wearable sensors can be integrated into clothing 10,32,38 and jewelry, 1,46 or worn as wearable devices in their own right. 5,22,23,25,30,45 Wearable sensors are attached to the subject they are monitoring and can therefore measure physiological/biomechanical parameters which may not be measurable using ambient sensors. However, the design of wearables is complicated by size, weight, and power consumption requirements.…”

Section: Wearable Systemsmentioning

confidence: 99%

A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

et al. 2006

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Rapid technological advances have prompted the development of a wide range of telemonitoring systems to enable the prevention, early diagnosis and management, of chronic conditions. Remote monitoring can reduce the amount of recurring admissions to hospital, facilitate more efficient clinical visits with objective results, and may reduce the length of a hospital stay for individuals who are living at home. Telemonitoring can also be applied on a long-term basis to elderly persons to detect gradual deterioration in their health status, which may imply a reduction in their ability to live independently. Mobility is a good indicator of health status and thus by monitoring mobility, clinicians may assess the health status of elderly persons. This article reviews the architecture of health smart home, wearable, and combination systems for the remote monitoring of the mobility of elderly persons as a mechanism of assessing the health status of elderly persons while in their own living environment.

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Section: Wearable Systemsmentioning

confidence: 99%

A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

et al. 2006

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“…For example, activity and rest were discriminated by integration of the area under the acceleration curves every second, to produce a measure of metabolic energy expenditure, and then comparison of the measured value with a predetermined threshold, if the measured value exceeded the preset threshold, then the subject was classified as engaged in activity. Otherwise, a classification of resting was made (MATHIE et al, 2003b(MATHIE et al, , 2002). An upright posture and lying were discriminated using the measured tilt angle of the subject.…”

Section: Medical and Biological Engineering And Computing 2004 Vol 42mentioning

confidence: 99%

“…Within this, there is a trend towards wearable systems that go beyond traditional physiological monitoring to include measures of environmental health, functional performance and activities related to daily living (WINTERS et al, 2003;MATHIE et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Classification of basic daily movements using a triaxial accelerometer

Mathie

Celler

Lovell

et al. 2004

Med. Biol. Eng. Comput.

387

211

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A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.

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“…Many studies have demonstrated the usefulness of accelerometry for the evaluation of physical activity, mostly focusing on the detection of level walking or active/rest discrimination (Tamura et al 1997;Nakahara et al 1999;Aminian et al 1999;Mathie et al 2002). However, with regard to vertical movements such as stair climbing, evaluation of energy consumption has been still insufficient even though stair climbing requires more than twice the energy of level walking.…”

Section: Introductionmentioning

confidence: 98%

Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer

et al. 2005

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This paper describes a method to evaluate daily physical activity by means of a portable device that determines the type of physical activity based on accelerometers and a barometer. Energy consumption of a given type of physical activity was calculated according to relative metabolic ratio (RMR) of each physical activity type that reflects exercise intensity of activities. Special attention was paid to classification algorithms for activity typing that identify detailed ambulatory movements considering vertical movements, such as stair/ slope climbing or use of elevators. A portable measurement device with accelerometers and a barometer, and a Kalman filter was designed to detect the features of vertical movements. Furthermore, walking speed was calculated by an equation which estimates the walking speed as a function of signal energy of vertical body acceleration during walking. To confirm the usefulness of the method, preliminary experiments were performed with healthy young and elderly subjects. The portable device was attached to the waist. A standard accelerometer based calorie counter was also attached for comparison. Experimental results showed that the proposed method feasibly classified the type of ambulatory physical activities; level walking, stair going up and down and elevator use. It was suggested that the consideration of vertical movements made a significant improvement in the estimation of energy consumptions, and the proposed method provides better estimation of physical activity compared to the conventional calorie counter.

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Determining activity using a triaxial accelerometer

Cited by 41 publications

References 4 publications

A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment

Classification of basic daily movements using a triaxial accelerometer

Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer

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