The Experts below are selected from a list of 7044 Experts worldwide ranked by ideXlab platform
Stephen J. Redmond - One of the best experts on this subject based on the ideXlab platform.
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low power fall detector using triaxial accelerometry and Barometric Pressure sensing
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, David C W Chang, Nigel H LovellAbstract:Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and Barometric Pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.
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Low-power operation of a Barometric Pressure sensor for use in an automatic fall detector
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016Co-Authors: Wei Lu, Michael C. Stevens, Changhong Wang, Stephen J. RedmondAbstract:The use of a Barometric Pressure sensor in a wearable fall detector has been shown to improve the detection accuracy by determining the altitude change associated with the fall event. However, the barometer is a high-power-consuming sensor. This paper proposes a fall detection approach using a hermetically sealed and waterproof enclosure incorporating a small window covered by a semi-permeable membrane (SPM) to delay the equilibrium of internal and external Pressures. This feature can be utilized to limit the time the barometer is powered but still capturing critical Pressure information to discriminate fall and non-fall events. The proposed fall detection system is evaluated with an existing data set of simulated fall and activities of daily living in which the Barometric Pressure data are delayed using a mathematical model of the enclosure and SPM assembly. Also, a benchtop test is performed to estimate the power and battery life. The proposed fall detection system achieves 94.0% sensitivity and 90.0% specificity with an estimated battery life of 995.7 days.
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a low power fall detection algorithm based on triaxial acceleration and Barometric Pressure
International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, Nigel H LovellAbstract:This paper proposes a low-power fall detection algorithm based on triaxial accelerometry and Barometric Pressure signals. The algorithm dynamically adjusts the sampling rate of an accelerometer and manages data transmission between sensors and a controller to reduce power consumption. The results of simulation show that the sensitivity and specificity of the proposed fall detection algorithm are both above 96% when applied to a previously collected dataset comprising 20 young actors performing a combination of simulated falls and activities of daily living. This level of performance can be achieved despite a 10.9% reduction in power consumption.
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energy expenditure estimation during normal ambulation using triaxial accelerometry and Barometric Pressure
Physiological Measurement, 2012Co-Authors: Jingjing Wang, Michael R. Narayanan, Matteo Voleno, Sergio Cerutti, Stephen J. Redmond, Ning Wang, Nigel H LovellAbstract:Energy expenditure (EE) is an important parameter in the assessment of physical activity. Most reliable techniques for EE estimation are too impractical for deployment in unsupervised free-living environments; those which do prove practical for unsupervised use often poorly estimate EE when the subject is working to change their altitude by walking up or down stairs or inclines. This study evaluates the augmentation of a standard triaxial accelerometry waist-worn wearable sensor with a Barometric Pressure sensor (as a surrogate measure for altitude) to improve EE estimates, particularly when the subject is ascending or descending stairs. Using a number of features extracted from the accelerometry and Barometric Pressure signals, a state space model is trained for EE estimation. An activity classification algorithm is also presented, and this activity classification output is also investigated as a model input parameter when estimating EE. This EE estimation model is compared against a similar model which solely utilizes accelerometry-derived features. A protocol (comprising lying, sitting, standing, walking, walking up stairs, walking down stairs and transitioning between activities) was performed by 13 healthy volunteers (8 males and 5 females; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system (K4b2, COSMED, Italy). Activity classification improves from 81.65% to 90.91% when including Barometric Pressure information; when analyzing walking activities alone the accuracy increases from 70.23% to 98.54%. Using features derived from both accelerometry and barometry signals, combined with features relating to the activity classification in a state space model, resulted in a estimation bias of −0.00 095 and precision (1.96SD) of 3.54 ml min−1 kg−1. Using only accelerometry features gives a relatively worse performance, with a bias of −0.09 and precision (1.96SD) of 5.99 ml min−1 kg−1, with the largest errors due to an underestimation of when walking up stairs.
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Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Federico Bianchi, Michael R. Narayanan, Stephen J. Redmond, Sergio CeruttiAbstract:Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a Barometric Pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air Pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.
Sergio Cerutti - One of the best experts on this subject based on the ideXlab platform.
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energy expenditure estimation during normal ambulation using triaxial accelerometry and Barometric Pressure
Physiological Measurement, 2012Co-Authors: Jingjing Wang, Michael R. Narayanan, Matteo Voleno, Sergio Cerutti, Stephen J. Redmond, Ning Wang, Nigel H LovellAbstract:Energy expenditure (EE) is an important parameter in the assessment of physical activity. Most reliable techniques for EE estimation are too impractical for deployment in unsupervised free-living environments; those which do prove practical for unsupervised use often poorly estimate EE when the subject is working to change their altitude by walking up or down stairs or inclines. This study evaluates the augmentation of a standard triaxial accelerometry waist-worn wearable sensor with a Barometric Pressure sensor (as a surrogate measure for altitude) to improve EE estimates, particularly when the subject is ascending or descending stairs. Using a number of features extracted from the accelerometry and Barometric Pressure signals, a state space model is trained for EE estimation. An activity classification algorithm is also presented, and this activity classification output is also investigated as a model input parameter when estimating EE. This EE estimation model is compared against a similar model which solely utilizes accelerometry-derived features. A protocol (comprising lying, sitting, standing, walking, walking up stairs, walking down stairs and transitioning between activities) was performed by 13 healthy volunteers (8 males and 5 females; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system (K4b2, COSMED, Italy). Activity classification improves from 81.65% to 90.91% when including Barometric Pressure information; when analyzing walking activities alone the accuracy increases from 70.23% to 98.54%. Using features derived from both accelerometry and barometry signals, combined with features relating to the activity classification in a state space model, resulted in a estimation bias of −0.00 095 and precision (1.96SD) of 3.54 ml min−1 kg−1. Using only accelerometry features gives a relatively worse performance, with a bias of −0.09 and precision (1.96SD) of 5.99 ml min−1 kg−1, with the largest errors due to an underestimation of when walking up stairs.
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Energy expenditure estimation using triaxial accelerometry and Barometric Pressure measurement
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Matteo Voleno, Stephen J. Redmond, Sergio CeruttiAbstract:Energy expenditure (EE) is a parameter of great relevance in studies involving the assessment of physical activity. However, most reliable techniques for EE estimation are impractical for use in free-living environments, and those which are practically useful often poorly track EE when the subject is working to change their altitude, for example when ascending or descending stairs or slopes. The aim of this study is to evaluate the utility of adding Barometric Pressure related features, as a surrogate measure for altitude, to existing accelerometry related features to estimate the subject's EE. The EE estimation system described is based on a triaxial accelerometer (triax) and a Barometric Pressure sensor. The device is wireless, with Bluetooth connectivity for data retrieval, and is mounted at the subject's waist. Using a number of features extracted from the triax and Barometric Pressure signals, a linear model is trained for EE estimation. This EE estimation model is compared to its counterpart, which solely utilizes accelerometry signals. A protocol (comprising lying, sitting, standing, walking phases) was performed by 13 healthy volunteers (8 male and 5 female; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system. The model incorporating Barometric Pressure information estimated the oxygen uptake with the lowest mean square error of 4.5±1.7 (mlO2.min-1.kg-1)2, in comparison to 7.1±2.3 (mlO2.min-1.kg-1)2 using only accelerometry-based features.
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Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Federico Bianchi, Michael R. Narayanan, Stephen J. Redmond, Sergio CeruttiAbstract:Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a Barometric Pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air Pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.
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Falls event detection using triaxial accelerometry and Barometric Pressure measurement
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009Co-Authors: Federico Bianchi, Michael R. Narayanan, Sergio Cerutti, Stephen J. Redmond, Branko G. CellerAbstract:A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a Barometric Pressure sensor, is described. The aim of this study is to evaluate the use of Barometric Pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and Barometric Pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 plusmn 2.9 years; height: 1.74 plusmn 0.11 m). The algorithm incorporating Pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).
Michael R. Narayanan - One of the best experts on this subject based on the ideXlab platform.
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low power fall detector using triaxial accelerometry and Barometric Pressure sensing
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, David C W Chang, Nigel H LovellAbstract:Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and Barometric Pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.
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a low power fall detection algorithm based on triaxial acceleration and Barometric Pressure
International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, Nigel H LovellAbstract:This paper proposes a low-power fall detection algorithm based on triaxial accelerometry and Barometric Pressure signals. The algorithm dynamically adjusts the sampling rate of an accelerometer and manages data transmission between sensors and a controller to reduce power consumption. The results of simulation show that the sensitivity and specificity of the proposed fall detection algorithm are both above 96% when applied to a previously collected dataset comprising 20 young actors performing a combination of simulated falls and activities of daily living. This level of performance can be achieved despite a 10.9% reduction in power consumption.
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energy expenditure estimation during normal ambulation using triaxial accelerometry and Barometric Pressure
Physiological Measurement, 2012Co-Authors: Jingjing Wang, Michael R. Narayanan, Matteo Voleno, Sergio Cerutti, Stephen J. Redmond, Ning Wang, Nigel H LovellAbstract:Energy expenditure (EE) is an important parameter in the assessment of physical activity. Most reliable techniques for EE estimation are too impractical for deployment in unsupervised free-living environments; those which do prove practical for unsupervised use often poorly estimate EE when the subject is working to change their altitude by walking up or down stairs or inclines. This study evaluates the augmentation of a standard triaxial accelerometry waist-worn wearable sensor with a Barometric Pressure sensor (as a surrogate measure for altitude) to improve EE estimates, particularly when the subject is ascending or descending stairs. Using a number of features extracted from the accelerometry and Barometric Pressure signals, a state space model is trained for EE estimation. An activity classification algorithm is also presented, and this activity classification output is also investigated as a model input parameter when estimating EE. This EE estimation model is compared against a similar model which solely utilizes accelerometry-derived features. A protocol (comprising lying, sitting, standing, walking, walking up stairs, walking down stairs and transitioning between activities) was performed by 13 healthy volunteers (8 males and 5 females; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system (K4b2, COSMED, Italy). Activity classification improves from 81.65% to 90.91% when including Barometric Pressure information; when analyzing walking activities alone the accuracy increases from 70.23% to 98.54%. Using features derived from both accelerometry and barometry signals, combined with features relating to the activity classification in a state space model, resulted in a estimation bias of −0.00 095 and precision (1.96SD) of 3.54 ml min−1 kg−1. Using only accelerometry features gives a relatively worse performance, with a bias of −0.09 and precision (1.96SD) of 5.99 ml min−1 kg−1, with the largest errors due to an underestimation of when walking up stairs.
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Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Federico Bianchi, Michael R. Narayanan, Stephen J. Redmond, Sergio CeruttiAbstract:Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a Barometric Pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air Pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.
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Falls event detection using triaxial accelerometry and Barometric Pressure measurement
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009Co-Authors: Federico Bianchi, Michael R. Narayanan, Sergio Cerutti, Stephen J. Redmond, Branko G. CellerAbstract:A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a Barometric Pressure sensor, is described. The aim of this study is to evaluate the use of Barometric Pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and Barometric Pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 plusmn 2.9 years; height: 1.74 plusmn 0.11 m). The algorithm incorporating Pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).
Federico Bianchi - One of the best experts on this subject based on the ideXlab platform.
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Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Federico Bianchi, Michael R. Narayanan, Stephen J. Redmond, Sergio CeruttiAbstract:Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a Barometric Pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air Pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.
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Falls event detection using triaxial accelerometry and Barometric Pressure measurement
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009Co-Authors: Federico Bianchi, Michael R. Narayanan, Sergio Cerutti, Stephen J. Redmond, Branko G. CellerAbstract:A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a Barometric Pressure sensor, is described. The aim of this study is to evaluate the use of Barometric Pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and Barometric Pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 plusmn 2.9 years; height: 1.74 plusmn 0.11 m). The algorithm incorporating Pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).
Nigel H Lovell - One of the best experts on this subject based on the ideXlab platform.
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low power fall detector using triaxial accelerometry and Barometric Pressure sensing
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, David C W Chang, Nigel H LovellAbstract:Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and Barometric Pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.
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a low power fall detection algorithm based on triaxial acceleration and Barometric Pressure
International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Co-Authors: Changhong Wang, Michael R. Narayanan, Stephen J. Redmond, Stephen R Lord, Nigel H LovellAbstract:This paper proposes a low-power fall detection algorithm based on triaxial accelerometry and Barometric Pressure signals. The algorithm dynamically adjusts the sampling rate of an accelerometer and manages data transmission between sensors and a controller to reduce power consumption. The results of simulation show that the sensitivity and specificity of the proposed fall detection algorithm are both above 96% when applied to a previously collected dataset comprising 20 young actors performing a combination of simulated falls and activities of daily living. This level of performance can be achieved despite a 10.9% reduction in power consumption.
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energy expenditure estimation during normal ambulation using triaxial accelerometry and Barometric Pressure
Physiological Measurement, 2012Co-Authors: Jingjing Wang, Michael R. Narayanan, Matteo Voleno, Sergio Cerutti, Stephen J. Redmond, Ning Wang, Nigel H LovellAbstract:Energy expenditure (EE) is an important parameter in the assessment of physical activity. Most reliable techniques for EE estimation are too impractical for deployment in unsupervised free-living environments; those which do prove practical for unsupervised use often poorly estimate EE when the subject is working to change their altitude by walking up or down stairs or inclines. This study evaluates the augmentation of a standard triaxial accelerometry waist-worn wearable sensor with a Barometric Pressure sensor (as a surrogate measure for altitude) to improve EE estimates, particularly when the subject is ascending or descending stairs. Using a number of features extracted from the accelerometry and Barometric Pressure signals, a state space model is trained for EE estimation. An activity classification algorithm is also presented, and this activity classification output is also investigated as a model input parameter when estimating EE. This EE estimation model is compared against a similar model which solely utilizes accelerometry-derived features. A protocol (comprising lying, sitting, standing, walking, walking up stairs, walking down stairs and transitioning between activities) was performed by 13 healthy volunteers (8 males and 5 females; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system (K4b2, COSMED, Italy). Activity classification improves from 81.65% to 90.91% when including Barometric Pressure information; when analyzing walking activities alone the accuracy increases from 70.23% to 98.54%. Using features derived from both accelerometry and barometry signals, combined with features relating to the activity classification in a state space model, resulted in a estimation bias of −0.00 095 and precision (1.96SD) of 3.54 ml min−1 kg−1. Using only accelerometry features gives a relatively worse performance, with a bias of −0.09 and precision (1.96SD) of 5.99 ml min−1 kg−1, with the largest errors due to an underestimation of when walking up stairs.