Long-term mobility monitoring of older adults using accelerometers in a clinical environment

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

doi:10.1191/0269215504cr734oa

Cited by 84 publications

(50 citation statements)

References 6 publications

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“…The ADXL210 produces representative analogue acceleration voltages at the X FILT and Y FILT outputs. The placement of the sensors at the trunk and thigh was chosen as a uni-axial version of this sensor arrangement (vertical accelerometer on trunk and thigh) has been shown to provide subject mobility data (amount of time spent sitting, standing, lying and walking) [23]. Thus, it was envisioned that this sensor arrangement would measure both mobility and detect falls.…”

Section: Data Acquisition Set-upmentioning

confidence: 99%

Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm

2007

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Section: Data Acquisition Set-upmentioning

confidence: 99%

Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm

2007

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“…31 Accelerometry is low-cost, flexible, and accurate method for the analysis of posture and movement, 24 with applications in fall detection, gait analysis, and monitoring of a variety of pathological conditions, such as COPD (Chronic Obstructive Pulmonary Disease). 5,25 Accelerometer-based systems have been shown to accurately measure both dynamic and static activities in both long 11,22 and shortterm situations. 30 Accelerometers operate by measuring acceleration along each axis of the device and can therefore detect static postures by measuring the acceleration due to gravity, and detect motion by measuring the corresponding dynamic acceleration.…”

Section: Wearable Sensorsmentioning

confidence: 99%

“…Veltink's configuration has been has been adapted by Culhane et al 11,20 and validated in a long-term clinical trial of elderly people. This configuration was found to have a detection accuracy of 96%, when compared to the observed data.…”

Section: Data Processing Wearablesmentioning

confidence: 99%

“…However, there is little consensus as to the optimal placement and amount of sensors required to obtain sufficient results; with some authors preferring a single sensor unit worn at the waist, 12,22,23,25,59 sacrum 43 or chest 28,31 to multiple sensors distributed on the body. 11,20,30,53 Data Logging Wearables Data logging systems have the advantage of being able to monitor the subject regardless of their location. The disadvantage of data logging systems is that the subject's mobility patterns cannot be analyzed between uploads.…”

Section: Wearable Sensorsmentioning

confidence: 99%

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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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“…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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Long-term mobility monitoring of older adults using accelerometers in a clinical environment

Abstract: In a population of older adults, the static activities of sitting, standing and lying and dynamic activities can be distinguished using the technique and threshold values outlined here to a degree of accuracy of 92% and higher.

Cited by 84 publications

References 6 publications

Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm

Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm

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

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

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