A description of an accelerometer-based mobility monitoring technique

Lyons, G.M.; Culhane, K.M.; Hilton, Deborah J; Grace, Pierce A.; Lyons, Declan

doi:10.1016/j.medengphy.2004.11.006

Cited by 192 publications

(140 citation statements)

References 18 publications

(27 reference statements)

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“…Even in terrestrial species, accelerometers can be used to record animal behavior automatically without human presence. Such devices have been employed to remotely measure animal walking distance (Rothwell et al 2011), animal orientation (Lyons et al 2005, Ringgenberg et al 2010, and animal activity level and metabolic rate (Wilson et al 2006, Halsey et al 2009, Enstipp et al 2011.…”

Section: Introductionmentioning

confidence: 99%

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Soltis¹,

Wilson²,

Douglas‐Hamilton³

et al. 2012

Endang. Species. Res.

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Accelerometers are motion-detection devices that, when attached to animals, are capable of detecting body orientation, overall activity levels, and specific behavior patterns. We deployed accelerometers in order to study the hypothesis that accelerometer output would allow us to distinguish between 4 behavior patterns in 3 adult female African elephants Loxodonta africana at Disney's Animal Kingdom ® , Florida, USA. Tri-axial accelerometer data loggers were attached to the tops of collars worn around the elephants' necks. Behavior was documented on video while the accelerometer output was stored on the data logger. Feeding, bathing, and walking behaviors were recorded in all 3 subjects, while swaying behavior could be recorded in only 1 subject. Data in the 3 physical dimensions (sway, surge, and heave) were analyzed in terms of overall magnitude of movement (dynamic acceleration) and in terms of the periodicity of movement. When classifying accelerometer data of unlabeled origin to the correct behavioral state, overall success ranged from 70 to 91%. Bathing was sometimes confused with feeding and walking, but feeding, walking, and swaying were easily distinguished from each other. These results show that data from accelerometers can distinguish an elephant's behavioral states, and thus may be used to monitor elephant behavior remotely. Such devices could be deployed for a variety of purposes, ranging from monitoring elephant activity in zoos to an early-warning system that could alert the authorities when wild elephants are being illegally hunted. KEY WORDS: Behavior measurement · Accelerometry · Elephant managementResale or republication not permitted without written consent of the publisher OPEN PEN

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Section: Introductionmentioning

confidence: 99%

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Soltis¹,

Wilson²,

Douglas‐Hamilton³

et al. 2012

Endang. Species. Res.

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“…With acceleration readings from Shimmer, we compute an inclination angle and a standard deviation with vertical and anterior-posterior accelerations to recognize real-time human physical activities. The inclination angle has been calculated to classify the static activity in previous research [5][6][7][8][9]. After obtaining each x-and y-axis of acceleration values, we calculate the inclination angle (Φ) with Eq.…”

Section: Metrics For Activity Recognitionmentioning

confidence: 99%

Understanding people with human activities and social interactions for human-centered computing

Choi

2016

Hum. Cent. Comput. Inf. Sci.

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Computing technology has changed a lot of things in the world quickly. It is still affecting the way we think, the way we communicate with each other, the way we study, the way we work, and so on. Furthermore, computer hardware and software keep evolving every day and new paradigms and mechanisms continuously emerge into the world. There is no doubt that the main purpose of these technologies make human beings and our daily life convenient. In other word, the center of computing is all mankind.However, the main purpose of computing technology may not be human beings. Artificial intelligence (AI) could be one of the promising computing technologies in the future. Many AI researchers hope that this technology will help and assist people effectively and efficiently. In addition, they believe that AI dramatically change our life in the near future. Unfortunately, all of the change are not always positive. Rapid and frequent changing of computing technologies could be difficult to use especially for elderly people. Furthermore, it is well known that innovative AI technology could give people a great fear. Some famous movies dealing with AI issues reflected this phenomenon. In addition, there was the fight of the century to play game Go between an AI Go player (AlphaGo) and a professional human Go player in March, 2016. AlphaGo finally defeated the human Go player at that game. It is definitely possible that the result of the game surprised and frightened all of the people in the world. AbstractAn ultimate goal of human-centered computing is making human beings the center of computing technologies. To make people a core component of the technology, first of all, we need to understand people and society in which people live. In this paper, we propose two important factors in order to understand people and their social interactions. Proposed human activity recognition and neighbor discovery schemes help us comprehend human activities and their social behaviors. In addition, combination of these two mechanisms provide us with an opportunity for better understanding of people in the near future. Finally, it might be worth analyzing correlation between human activities and social interactions for the group of people using our proposed schemes.

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“…When using a single waist or ankle accelerometer, lying down was determined when the waist or ankle angle was between 50 and 130 deg, with undefined orientations for waist or ankle angles greater than 130 deg and upright postures between 0 and 50 deg ( Fig. 2(a)) [30]. When using only the thigh accelerometer, standing and sitting/lying were differentiated based on the thigh angle, in relation to gravity, of less than 45 deg or greater than 45 deg, respectively [30] (Fig.…”

Section: Signal Processingmentioning

confidence: 99%

“…2(a)) [30]. When using only the thigh accelerometer, standing and sitting/lying were differentiated based on the thigh angle, in relation to gravity, of less than 45 deg or greater than 45 deg, respectively [30] (Fig. 2(b)).…”

Section: Signal Processingmentioning

confidence: 99%

Posture and Movement Classification: The Comparison of Tri-Axial Accelerometer Numbers and Anatomical Placement

Fortune

Lugade

Kaufman

2014

Journal of Biomechanical Engineering

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Patient compliance is important when assessing movement, particularly in a free-living environment when patients are asked to don their own accelerometers. Reducing the number of accelerometers could increase patient compliance. The aims of this study were (1) to determine and compare the validity of different accelerometer combinations and placements for a previously developed posture and dynamic movement identification algorithm. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, right thigh, and waist of 12 healthy adults. Subjects performed a protocol in the laboratory including static orientations of standing, sitting, and lying down, and dynamic movements of walking, jogging, transitions between postures, and fidgeting to simulate free-living activity. When only one accelerometer was used, the thigh was found to be the optimal placement to identify both movement and static postures, with a misclassification error of 10%, and demonstrated the greatest accuracy for walking/fidgeting and jogging classification with sensitivities and positive predictive value (PPVs) greater than 93%. When two accelerometers were used, the waist-thigh accelerometers identified movement and static postures with greater accuracy than the thigh-ankle accelerometers (with a misclassification error of 11% compared to 17%). However, the thigh-ankle accelerometers demonstrated the greatest accuracy for walking/ fidgeting and jogging classification with sensitivities and PPVs greater than 93%. Movement can be accurately classified in healthy adults using tri-axial accelerometers placed on one or two of the following sites: waist, thigh, or ankle. Posture and transitions require an accelerometer placed on the waist and an accelerometer placed on the thigh.

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A description of an accelerometer-based mobility monitoring technique

Cited by 192 publications

References 18 publications

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Understanding people with human activities and social interactions for human-centered computing

Posture and Movement Classification: The Comparison of Tri-Axial Accelerometer Numbers and Anatomical Placement

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