25 research outputs found

    Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

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    Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

    Smartwatch-Based IoT Fall Detection Application

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    This paper proposes using only the streaming accelerometer data from a commodity-based smartwatch (IoT) device to detect falls. The smartwatch is paired with a smartphone as a means for performing the computation necessary for the prediction of falls in realtime without incurring latency in communicating with a cloud server while also preserving data privacy. The majority of current fall detection applications require specially designed hardware and software which make them expensive and inaccessible to the general public. Moreover, a fall detection application that uses a wrist worn smartwatch for data collection has the added benefit that it can be perceived as a piece of jewelry and thus non-intrusive. We experimented with both Support Vector Machine and Naive Bayes machine learning algorithms for the creation of the fall model. We demonstrated that by adjusting the sampling frequency of the streaming data, computing acceleration features over a sliding window, and using a Naive Bayes machine learning model, we can obtain the true positive rate of fall detection in real-world setting with 93.33% accuracy. Our result demonstrated that using a commodity-based smartwatch sensor can yield fall detection results that are competitive with those of custom made expensive sensors

    Towards a smart fall detection system using wearable sensors

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    Empirical thesis."A thesis submitted as part of a cotutelle programme in partial fulfilment of Coventry University’s and Macquarie University’s requirements for the degree of Doctor of Philosophy" -- title page.Bibliography: pages 183-205.1. Introduction -- 2. Literature review -- 3. Falls and activities of daily living datasets -- 4. An analysis of fall-detection approaches -- 5. Event-triggered machine-learning approach (EvenT-ML) -- 6. Genetic-algorithm-based feature-selection technique for fall detection (GA-Fade) -- 7. Conclusions and future work -- References -- Appendices.A fall-detection system is employed in order to monitor an older person or infirm patient and alert their carer when a fall occurs. Some studies use wearable-sensor technologies to detect falls, as those technologies are getting smaller and cheaper. To date, wearable-sensor-based fall-detection approaches are categorised into threshold and machine-learning-based approaches. A high number of false alarms and a high computational cost are issues that are faced by the threshold- and machine-learning basedapproaches, respectively. The goal of this thesis is to address those issues by developing a novel low-computational-cost machine-learning-based approach for fall detection using accelerometer sensors.Toward this goal, existing fall-detection approaches (both threshold- and machine-learning-based) are explored and evaluated using publicly accessible datasets: Cogent, SisFall, and FARSEEING. Four machine-learning algorithms are implemented in this study: Classification and Regression Tree (CART), k-Nearest Neighbour (k-NN), Logistic Regression (LR), and Support Vector Machine (SVM). The experimental results show that using the correct size and type for the sliding window to segment the data stream can give the machine-learning-based approach a better detection rate than the threshold-based approach, though the difference between the threshold- and machine-learning-based approaches is not significant in some cases.To further improve the performance of the machine-learning-based approaches, fall stages (pre-impact, impact, and post-impact) are used as a basis for the feature extraction process. A novel approach called an event-triggered machine-learning approach for fall detection (EvenT-ML) is proposed, which can correctly align fall stages into a data segment and extract features based on those stages. Correctly aligning the stages to a data segment is difficult because of multiple high peaks, where a high peak usually indicates the impact stage, often occurring during the pre-impact stage. EvenT-ML significantly improves the detection rate and reduces the computational cost of existing machine-learning-based approaches, with an up to 97.6% F-score and a reduction in computational cost by a factor of up to 80 during feature extraction. Also, this technique can significantly outperform the threshold-based approach in all cases.Finally, to reduce the computational cost of EvenT-ML even further, the number of features needs to be reduced through a feature-selection process. A novel genetic-algorithm-based feature-selection technique (GA-Fade) is proposed, which uses multiple criteria to select features. GA-Fade considers the detection rate, the computational cost, and the number of sensors used as the selection criteria. GAFade is able to reduce the number of features by 60% on average, while achieving an F-score of up to 97.7%. The selected features also can give a significantly lower total computational cost than features that are selected by two single-criterion-based feature-selection techniques: SelectKBest and Recursive Feature Elimination.In summary, the techniques presented in this thesis significantly increase the detection rate of the machine-learning-based approach, so that a more reliable fall detection system can be achieved. Furthermore, as an additional advantage, these techniques can significantly reduce the computational cost of the machine-learning approach. This advantage indicates that the proposed machine-learning-based approach is more applicable to a small wearable device with limited resources (e.g., computing power and battery capacity) than the existing machine-learning-based approaches.Mode of access: World wide web1 online resource (xx, 211 pages) diagrams, graphs, table

    How to build better fall detection technology : a search for characteristics unique to falls and methods to robustly evaluate performance

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    Falls can have severe consequences for older adults, such as bone fractures and long periods unable to get up from the ground, known as a long-lie. The capability to automatically detect falls would reduce long-lies through ensuring prompt arrival of assistance and would be valuable in fall risk assessment and fall prevention research. This research aimed to identify why existing wearable fall detection technology has not achieved acceptable performance and where further development should focus. There have been a plethora of attempts at fall detection; real-world testing is in an embryonic stage, nevertheless, it is clear performance has been poor. The focus has been on the testing of complete system performance, most commonly with acted falls, and it has been unclear how to improve performance. A new framework for the development of fall detection is proposed which promotes targeted investigation of how real-world performance can be improved. An improved method to quantify real-world performance is also proposed based on a systematic review of previous approaches. To prepare for the analysis of a real-world dataset, a pilot study was conducted which focused on the development and testing of posture classification algorithms. One of the world's largest datasets of real-world falls and activities of daily living was collected over 2 years in collaboration with 17 care homes across Scotland and the north of England. Twenty fall signals were extracted from 1,919 days of thigh-worn accelerometer recordings collected with 42 participants. Analysis of the data focused on falls from an upright to a sedentary (sitting or lying) posture, 16 falls met this criterion and were included in the analysis. To allow the data to be thoroughly checked for quality, the dataset was reduced to 104 days, from which 4,293 upright to sedentary transitions were extracted (including the 16 falls). This study was the first to: discern that falls may be too diverse to classify as a single group and focus on a subtype of fall, use posture transitions to select events for analysis, assess the importance of peak jerk and vertical velocity for fall detection, and investigate the occurrence of multiple impacts during falls. The results demonstrated that the core features used previously do not yield sufficient separation of the falls to allow detection without high rates of false positives. For the first time, it was shown that (1) a rapid increase in deceleration may be more indicative of a fall than the peak deceleration, and (2) multiple impacts occur frequently in falls but not other movements

    Elderly Fall Detection Systems: A Literature Survey

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    Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial

    Validation of Accuracy of SVM-Based Fall Detection System Using Real-World Fall and Non-Fall Datasets

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    Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets

    Wearables for independent living in older adults: Gait and falls

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    Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised
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