Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector Machines

Aziz, Omar; Park, Edward J.; Mori, Greg; Robinovitch, Stephen N.

doi:10.1109/embc.2012.6347321

Cited by 17 publications

(35 citation statements)

References 12 publications

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“…Apart from the different methods and predictors that were considered to be good in these studies [14][15][16] , all of our studies did find that the analysis of trunk acceleration may have the potential for evaluating the fall risk.…”

Section: Discussionmentioning

confidence: 98%

“…From the few studies that have used accelerometers to assess near-falls 15,16 , Weiss et al 14 were one of the first. Their results indicated that the maximum peak to peak difference in vertical jerk was the best single parameter to distinguish a near-fall from a non-fall.…”

Section: Discussionmentioning

confidence: 99%

“…More recent studies 15,16 used accelerometers and other worn body-sensors to differentiate nearfalls (i.e. imbalance) from falls and activities of daily living.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Trip Severity Based on Tri-Axial Accelerometry in Healthy Older Adults

Antunes

Kesteren

2015

J. Hum. Growth Dev.

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Introduction: falls among elderly are apublic health problem and fall prevention is of utmost importance. The ability to recovery from a trip or not might be indicative for fall risk. Objective: toanalyse the relationship between trunk accelerations during the initial phase of tripping and the severity of a tripin healthy older adults. Methods: fourteen healthy older adults (65-73 yrs)walked multiple times over a platform with embedded obstacles and were tripped while trunk accelerations were assessed. Supported bodyweight (BW) by a safety harness was used to classify severity of the tripping outcome into high (>50%BW) or low (<50%BW). Twelve parameters obtained from the acceleration signals and their derivatives (jerk) within the first second after tripping initiation and were divided into three levels of parameter values with equal amount of trials. These low, medium and high values were tested for their association with trip severity in a logistic regression analysis. Results: three acceleration parametersappeared to be significant predictors oftrip severity. High values of minimum anterior-posterior acceleration and minimum vertical jerkshowed lower likelihood of resulting in a high severity trip than in the low values (33% and 32%, respectively). Medium values of the maximum anterior-posterior accelerationshowed higher likelihood of resulting in a high severity trip than the low values (327%).Conclusion: high acceleration and jerk peaks detected within the first second after tripping predict a more severe outcome, indicating that trunk tri-axial accelerometryhas the potential to predict the severity oftripping outcome in healthy older adults.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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Prediction of Trip Severity Based on Tri-Axial Accelerometry in Healthy Older Adults

Antunes

Kesteren

2015

J. Hum. Growth Dev.

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“…In this respect, Table 1 summarizes state-of-the-art solutions [11][12][13][14][15] declared to be able to recognize near falls. The table reports the architectures in terms of used acquisition equipment, fall indicators (i.e., the feature(s) to be monitored and classified) and chosen classification method.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the last two rows summarize the declared system performance (i.e., accuracy and efficiency) and the applicability of proposed systems to daily-life and/or ambulatory contexts, as well as their suitability in the context of real-time near falls detection and fall prevention. All the studies selected for the comparison analyze unexpected slippages, classifying them as near falls because all the perturbations analyzed in [11][12][13][14][15] led to balance recovery. Table 1 shows that the most used technologies in the loss of balance detection are motion capture systems (MCS) [12,13,15] and inertial measurement units (IMU) [11,14].…”

Section: Introductionmentioning

confidence: 99%

High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

Venuto

Mezzina

2020

Sensors

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Falls are a significant cause of loss of independence, disability and reduced quality of life in people with Parkinson’s disease (PD). Intervening quickly and accurately on the postural instability could strongly reduce the consequences of falls. In this context, the paper proposes and validates a novel architecture for the reliable recognition of losses of balance situations. The proposed system addresses some challenges related to the daily life applicability of near-fall recognition systems: the high specificity and system robustness against the Activities of Daily Life (ADL). In this respect, the proposed algorithm has been tested on five different tasks: walking steps, sudden curves, chair transfers via the timed up and go (TUG) test, balance-challenging obstacle avoidance and slip-induced loss of balance. The system analyzes data from wireless acquisition devices that capture electroencephalography (EEG) and electromyography (EMG) signals. The collected data are sent to two main units: the muscular unit and the cortical one. The first realizes a binary ON/OFF pattern from muscular activity (10 EMGs) and triggers the cortical unit. This latter unit evaluates the rate of variation in the EEG power spectrum density (PSD), considering five bands of interest. The neuromuscular features are then sent to a logical network for the final classification, which distinguishes among falls and ADL. In this preliminary study, we tested the proposed model on 9 healthy subjects (aged 26.3 ± 2.4 years), even if the study on PD patients is under investigation. Experimental validation on healthy subjects showed that the system reacts in 370.62 ± 60.85 ms with a sensitivity of 93.33 ± 5.16%. During the ADL tests the system showed a specificity of 98.91 ± 0.44% in steady walking steps recognition, 99.61 ± 0.66% in sudden curves detection, 98.95 ± 1.27% in contractions related to TUG tests and 98.42 ± 0.90% in the obstacle avoidance protocol.

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A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials

Aziz

Musngi

Park

et al. 2016

Med Biol Eng Comput

164

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Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

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Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector Machines

Cited by 17 publications

References 12 publications

Prediction of Trip Severity Based on Tri-Axial Accelerometry in Healthy Older Adults

Prediction of Trip Severity Based on Tri-Axial Accelerometry in Healthy Older Adults

High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials

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