Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data

Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds

Gait speed assessed by a 4-m walk test is not representative of daily-life gait speed in community-dwelling adults

Objectives: Standardized tests of gait speed are regarded as being of clinical value, but they are typically performed under optimal conditions, and may not reflect daily-life gait behavior. The aim of this study was to compare 4-m gait speed to the distribution of daily-life gait speed. Study design: The cross-sectional Grey Power cohort included 254 community-dwelling participants aged 18 years or more. Main outcome measures: Pearson's correlations were used to compare gait speed assessed using a timed 4-m walk test at preferred pace, and daily-life gait speed obtained from tri-axial lower-back accelerometer data over seven consecutive days. Results: Participants (median age 66.7 years [IQR 59.4–72.5], 65.7% female) had a mean 4-m gait speed of 1.43 m/s (SD 0.21), and a mean 50th percentile of daily-life gait speed of 0.90 m/s (SD 0.23). Ninety-six percent had a bimodal distribution of daily-life gait speed, with a mean 1st peak of 0.61 m/s (SD 0.15) and 2nd peak of 1.26 m/s (SD 0.23). The percentile of the daily-life distribution that corresponded best with the individual 4-m gait speed had a median value of 91.2 (IQR 75.4–98.6). The 4-m gait speed was very weakly correlated to the 1st and 2nd peak (r = 0.005, p = 0.936 and r=0.181, p = 0.004), and the daily-life gait speed percentiles (range: 1st percentile r = 0.076, p = 0.230 to 99th percentile r = 0.399, p < 0.001; 50th percentile r = 0.132, p = 0.036). Conclusions: The 4-m gait speed is only weakly related to daily-life gait speed. Clinicians and researchers should consider that 4-m gait speed and daily-life gait speed represent two different constructs

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Improved Screening of Fall Risk using Free-Living based Accelerometer Data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively

Improved screening of fall risk using free-living based accelerometer data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively