Accelerometer-based fall detection for smartphones
11 Jun 2014-pp 1-6
TL;DR: A unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm and can reliably detect fall events without disturbing the users with excessive false alarms.
Abstract: Falls are considered the main cause of fear and loss of independence among the elderly population and are also a major cause of morbidity, disability and health care utilization. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm. The data from the smartphone built-in accelerometer is continuously screened when the phone is in the user's belt or pocket. Upon the detection of a fall event, the user location is tracked and SMS and email notifications are sent to a set of contacts. The accuracy of the fall detection algorithm here proposed is near 97.5% for both the pocket and belt usage. In conclusion, the proposed solution can reliably detect fall events without disturbing the users with excessive false alarms, presenting also the advantage of not changing the user's routines, since no additional external sensors are required.
Citations
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TL;DR: An overview of fall detection research is presented and the core research questions on this topic are discussed and the most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance.
Abstract: Falling, as one of the main harm threats to the elderly, has drawn researchers’ attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.
117 citations
Cites background or methods from "Accelerometer-based fall detection ..."
...[18] chose DT for retrieving features and threshold information to detect a fall as well....
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...[18] proposed a fall detection system based on smart phone....
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...[18] Accelerometer Decision Tree (DT) 97....
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TL;DR: This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs and reveals the impact of the sensor range on the reliability of the traces.
Abstract: Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.
111 citations
Cites background from "Accelerometer-based fall detection ..."
...Authors in [51] showed that the performance of a smartphone-based detector is noticeable affected when the device shifts within the pocket....
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TL;DR: An innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of low-power wireless sensor networks, smart devices, big data and cloud computing.
99 citations
TL;DR: IoTE-Fall system is proposed, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm.
Abstract: Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.
93 citations
Cites background or methods or result from "Accelerometer-based fall detection ..."
...dressing many problems, including fall detection, in works such as [16, 19, 43, 51, 52]....
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...In addition, as comparate to Aguiar’s [16] approach, we demonstrated that several decision trees increase the classifier stability and accuracy thanks to plurality voting effectiveness....
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...We can note also that our ensemble-based system highlights better performance than the other four related works analyzed [12] [16] [19, 28] in terms of accuracy and sensitivity with a difference in the specificity of 5....
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...[16] used a smartphone built-in accelerometer for continuously monitor-...
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...[16] used a smartphone as execution environment for fall data processing....
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01 Sep 2016
TL;DR: In this paper, the recognition of and the differentiation between fall activities and activities of daily living (ADL) was performed using the MobiFall dataset and five different classification algorithms were implemented and evaluated based on their accuracy' sensitivity, and specificity achieved.
Abstract: In this paper, the recognition of and the differentiation between fall activities and activities of daily living (ADL) was performed using the MobiFall dataset. A large database was constructed to train and validate the model. Feature selection methods were implemented to reduce dimensionality. Five different classification algorithms were implemented and evaluated based on their accuracy' sensitivity, and specificity achieved. The k-Nearest Neighbors' algorithm obtained an overall accuracy of 87.5% with a sensitivity of 90.70%, and a specificity of 83.78%.
68 citations
Cites background from "Accelerometer-based fall detection ..."
...These sensors provide better computational capabilities when compared to other wearable devices [2], [3], [4]....
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References
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TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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12,243 citations
"Accelerometer-based fall detection ..." refers methods in this paper
...We tested three different offline machine learning classifiers to address this problem: Decision Trees [20], K-Nearest-Neighbours (KNN) [21] and Naı̈ve Bayes [22]....
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TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
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4,775 citations
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TL;DR: A proposed schema and some detailed specifications for constructing a learning system by means of programming a computer are given, trying to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality.
Abstract: This paper reports on a proposed schema and gives some detailed specifications for constructing a learning system by means of programming a computer. We have tried to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality, i.e., in order to achieve a system capable of performing in a wide variety of learning and problem-solving situations. Behavior of the system is determined by both a direct and an indirect means. The former involves detailed, explicit specification of responses or response patterns in the form of built-in programs. The indirect means is by programs representing three mechanisms: a “community unit” (a program-providing mechanism), a planning mechanism, and an induction mechanism. These mechanisms have in common the following features: (1) a directly given repertory of response patterns; (2) general and less explicitly specified decision making rules and hierarchically distributed authority for decision making; (3) an ability to delegate some control over the system's behavior to the environment; and (4) a self-modifying ability which allows the decision-making rules and the repertory of response patterns to adapt and grow. In Part I of this paper, the community unit is described and an illustration of its operation is given. It is presented in a schematized framework as a team of routines connected by first and second-order feedback loops. The function of the community unit is to provide higher-level programs (its environment or customers) with programs capable of performing requested tasks, or to perform a customer-stipulated task by executing a program. If the community unit does not have a ready-made program in stock to fill a particular request, internal programming will be performed, i.e., the community unit will have to construct a program, and debug it, before outputting or executing it. The primary purpose of internal programming is to assist higher-level programs in performing tasks for which detailed preplanning by an external programmer is either impossible or impractical. Some heuristics are suggested for enabling the community unit to search for a usable sequence of operations more efficiently than if it were to search simply by exhaustive or random trial and error. These heuristics are of a step-by-step nature. For complex problems, however, such step-by-step heuristics alone will fail unless there is also a mechanism for analyzing problem structure and placing guideposts on the road to the goal. A planning mechanism capable of doing this is proposed in Part II. Under the control of a higher-level program which specifies the level of detail required in a plan being developed, this planning mechanism is to break up problems into a hierarchy of subproblems each by itself presumably easier to solve than the original problem. To manage classes of problems and to make efficient use of past experience, an induction mechanism is proposed in Part II. An illustration is given of the induction mechanism solving a specific sequence of tasks. The system is currently being programmed and tested in IPL-V on the Philco 2000 computer. The current stage of the programming effort is reported in an epilogue to Part II.
3,719 citations
"Accelerometer-based fall detection ..." refers methods in this paper
...We tested three different offline machine learning classifiers to address this problem: Decision Trees [20], K-Nearest-Neighbours (KNN) [21] and Naı̈ve Bayes [22]....
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01 Jan 2006
TL;DR: Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.
Abstract: The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence
1,334 citations
TL;DR: Smartphone-based healthcare technologies as discussed in academic literature according to their functionalities are classified, and the disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.
Abstract: Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.
1,007 citations
"Accelerometer-based fall detection ..." refers background in this paper
...Although there are some smartphone applications for fall detection [18], most of them lack a representative dataset and consensual methodologies for the validation protocol [7]....
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