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      Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

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          Abstract

          Background

          Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting. Such approaches may provide a limited ability to detect falls in amputees and in real-world scenarios.

          Objective

          The aim was to develop a classifier that uses data from able-bodied individuals to detect falls in individuals with a lower limb amputation, while they freely carry the mobile phone in different locations and during free-living.

          Methods

          We obtained 861 simulated indoor and outdoor falls from 10 young control (non-amputee) individuals and 6 individuals with a lower limb amputation. In addition, we recorded a broad database of activities of daily living, including data from three participants’ free-living routines. Sensor readings (accelerometer and gyroscope) from a mobile phone were recorded as participants freely carried it in three common locations—on the waist, in a pocket, and in the hand. A set of 40 features were computed from the sensors data and four classifiers were trained and combined through stacking to detect falls. We compared the performance of two population-specific models, trained and tested on either able-bodied or amputee participants, with that of a model trained on able-bodied participants and tested on amputees. A simple threshold-based classifier was used to benchmark our machine-learning classifier.

          Results

          The accuracy of fall detection in amputees for a model trained on control individuals (sensitivity: mean 0.989, 1.96*standard error of the mean [SEM] 0.017; specificity: mean 0.968, SEM 0.025) was not statistically different ( P=.69) from that of a model trained on the amputee population (sensitivity: mean 0.984, SEM 0.016; specificity: mean 0.965, SEM 0.022). Detection of falls in control individuals yielded similar results (sensitivity: mean 0.979, SEM 0.022; specificity: mean 0.991, SEM 0.012). A mean 2.2 (SD 1.7) false alarms per day were obtained when evaluating the model (vs mean 122.1, SD 166.1 based on thresholds) on data recorded as participants carried the phone during their daily routine for two or more days. Machine-learning classifiers outperformed the threshold-based one ( P<.001).

          Conclusions

          A mobile phone-based fall detection model can use data from non-amputee individuals to detect falls in individuals walking with a prosthesis. We successfully detected falls when the mobile phone was carried across multiple locations and without a predetermined orientation. Furthermore, the number of false alarms yielded by the model over a longer period of time was reasonably low. This moves the application of mobile phone-based fall detection systems closer to a real-world use case scenario.

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          Most cited references29

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          XGBoost: A Scalable Tree Boosting System

          Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
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            Sparse multinomial logistic regression: fast algorithms and generalization bounds.

            Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency of the proposed methods.
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              Risk factors for serious injury during falls by older persons in the community.

              Serious fall injury represents a little studied, yet common and potentially preventable, cause of morbidity and mortality among older persons. We determined the frequency of, and risk factors for, experiencing serious fall injury events among older persons in the community. A representative sample of 1103 community-living persons aged 72 years and older underwent comprehensive baseline and 1-year evaluations. During a median 31 months of follow-up, fall data were obtained using fall calendars. Injury data were obtained from telephone interviews and from surveillance of emergency room and hospital records. At least one fall was experienced by 546 (49%) participants. A total of 123 participants, representing 23% of fallers and 12% of the cohort, experienced 183 serious fall injury events. The factors independently associated with experiencing a serious injury during a fall included cognitive impairment (adjusted odds ratios 2.2; 95% confidence interval 1.5, 3.2); presence of at least two chronic conditions (2.0; 1.4, 2.9); balance and gait impairment (1.8; 1.3, 2.7); and low body mass index (1.8; 1.2, 2.5). In a separate analysis, including only subjects who fell, female gender (1.8; 1.1, 2.9) as well as most of the above factors were associated with experiencing a fall injury. Several readily identifiable factors appeared to distinguish the subgroup of older fallers at risk for suffering a serious fall injury. These factors should help guide who and what to target in prevention efforts.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                October 2017
                11 October 2017
                : 5
                : 10
                : e151
                Affiliations
                [1] 1 Max Nader Lab for Rehabilitation Technologies and Outcomes Research Shirley Ryan AbilityLab Chicago, IL United States
                [2] 2 Center for Bionic Medicine Shirley Ryan AbilityLab Chicago, IL United States
                [3] 3 Department of Physical Medicine and Rehabilitation Northwestern University Chicago, IL United States
                [4] 4 Department of Computer Science Loyola University Chicago Chicago, IL United States
                [5] 5 Department of Bioengineering University of Pennsylvania Philadelphia, PA United States
                [6] 6 Department of Neuroscience University of Pennsylvania Philadelphia, PA United States
                [7] 7 Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago, IL United States
                Author notes
                Corresponding Author: Luca Lonini llonini@ 123456ricres.org
                Author information
                http://orcid.org/0000-0003-0351-9011
                http://orcid.org/0000-0002-9358-1718
                http://orcid.org/0000-0001-7695-6381
                http://orcid.org/0000-0002-3063-8107
                http://orcid.org/0000-0003-3977-2895
                http://orcid.org/0000-0001-8408-4499
                http://orcid.org/0000-0002-9302-6693
                Article
                v5i10e151
                10.2196/mhealth.8201
                5656773
                29021127
                35895b88-8e40-4070-a277-2778ebd27d1d
                ©Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.10.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 13 June 2017
                : 1 August 2017
                : 8 August 2017
                : 10 August 2017
                Categories
                Original Paper
                Original Paper

                fall detection,lower limb amputation,mobile phones,machine learning

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