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(searched for: doi:10.4236/ojab.2014.34004)
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Published: 23 March 2022
by MDPI
Applied Sciences, Volume 12; https://doi.org/10.3390/app12073276

Abstract:
Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.
Published: 6 July 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21144638

Abstract:
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.
International Journal of Precision Engineering and Manufacturing, Volume 21, pp 1985-1995; https://doi.org/10.1007/s12541-020-00398-6

The publisher has not yet granted permission to display this abstract.
Y. S. Kong, , , , S. M. Haris
Journal of Mechanical Science and Technology, Volume 33, pp 5137-5145; https://doi.org/10.1007/s12206-019-1003-9

The publisher has not yet granted permission to display this abstract.
Rashed- Al- Mahfuz, Robiul Hoque, Bimal Kumar Pramanik, Ekramul Hamid, Mohammad Ali Moni
2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) pp 1-5; https://doi.org/10.1109/ic4me247184.2019.9036529

Abstract:
Falls are a dangerous problem for people of all ages. Thus, accurate falls detection with minimized false alarms is very important. This study aims to detect falls and activities of daily living (ADLs) using acceleration data and to introduce an effective feature selection criterion to reduce the false positive rate of the falls detection systems. The falls detection system in this study consists of three stages. At the first stage, we have harnessed some feature extraction techniques to have discriminative features from the acceleration data. Then we have used feature selection criterions to select effective features in the detection task. At the last stage, we used Support Vector Machine (SVM) to classify the selected features in falls and ADLs. We have used raw acceleration data and extracted all the features. Then we selected features based on the Minimum Redundancy Maximum Relevance (MRMR) criterion and Double Input Symmetrical Relevance (DISR) in the fall detection experiment. We have found that the DISR feature selection criterion is more effective in acceleration based fall detection system. The results show 100% classification accuracy and zero false positive rates in fall detection for the DISR based selected features.
Published: 26 February 2019
by MDPI
Biosensors, Volume 9; https://doi.org/10.3390/bios9010029

Abstract:
Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.
Mingyue Peng, Dawu Lv, Dan Xiong, , Weijie Song,
Published: 18 January 2019
Journal of Electronic Materials, Volume 48, pp 2373-2381; https://doi.org/10.1007/s11664-019-06938-9

The publisher has not yet granted permission to display this abstract.
, , Farhana Zulkernine, Pete Nicholls
2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) pp 588-594; https://doi.org/10.1109/iemcon.2018.8614822

Abstract:
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called “MobiAct”, which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with Mbientlab's wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.
, Rajesh Kumar,
The Computer Journal, Volume 61, pp 1683-1694; https://doi.org/10.1093/comjnl/bxy022

Abstract:
Cerebral palsy (CP) is a neuro-development disease in children. It is quite an intricate task to categorize gait pattern into normal and CP based pathology. In this study, nature-inspired meta-heuristic algorithms are explored on a publicly available gait dataset of 156 subjects for automatic gait profiling of children with cerebral palsy. Five cases are considered to explore the feature selection criteria before applying clustering technique. Finding the optimal number of clusters is a challenging task in the unsupervised learning area. In this study, an optimal number of gait profiles in the datasets is identified based on voting from mean square error, silhouette coefficient and Dunn index. The study demonstrates that optimized based gait profile clusters could assist quantitatively in clinical rehabilitation evaluation for the children affected by CP.
Lulu Wang, Zhiwu Huang, Shuai Hao, Yijun Cheng, Yingze Yang, Hunan Engineering Laboratory of Rail Vehicles Braking Technology
Journal of Advanced Computational Intelligence and Intelligent Informatics, Volume 22, pp 88-96; https://doi.org/10.20965/jaciii.2018.p0088

Abstract:
Lower extremity fatigue is a risk factor for falls and injuries. This paper proposes a machine learning system to detect fatigue states, which considers the different influences of common daily activities on physical health. A wearable inertial unit is devised for gait data acquisition. The collected data are reorganized into nine data subsets for dimension reduction, and then preprocessed via gait cycle division, visualization, and oversampling. Then, a heterogeneous ensemble learning voting method is employed to train nine classifiers. The results indicate that the method reaches an accuracy of 92%, which is obtained by the plurality voting method using data subset prediction classes. Comparing the results shows that the final result is more accurate than the results of each individual data subset, and the heterogeneous voting method is advantageous when balancing out individual weaknesses of a set of equally well-performing models.
, Natalia Mordvanyuk, , , , Herman R. Holstlag
Published: 1 December 2017
Neurocomputing, Volume 268, pp 109-115; https://doi.org/10.1016/j.neucom.2016.11.084

The publisher has not yet granted permission to display this abstract.
Jianwen Song, Bo Wang, Li Xu, Meiling Chen, Zhigeng Pan
2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) pp 1-6; https://doi.org/10.1109/healthcom.2017.8210828

Abstract:
With the process of China's entering a serious aging society, it generally becomes one of the problems for people caring as elderly falls frequently. Thus, it is necessary for us find a way that the old people get help timely when it happened. On studies related to falls in outdoors currently, popular methods are on Computer Vision and Inertial Sensor. The problems of these researches are focus on the fall phenomenon of elderly mostly, lacking of deep excavation and analysis of experimental data. This paper is bringing forward Ë(() Set theory that means `Changing Set', which forms a dynamic set of the interrelated elements with FR (Frame of reference), D(t) (Experimental numerical set), T (Computing method set) and P (Results reasoning analysis set). We study the reasoning algorithmic using Ë(t) Set with normalized component data (both the angular velocity and acceleration from Inertial Sensor), to define feature points of the Ë(t) Set and determine the automatic alarm status of the fall time of the elderly.
Published: 25 September 2017
by MDPI
Applied Sciences, Volume 7; https://doi.org/10.3390/app7100986

Abstract:
New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care.
Published: 29 November 2016
by MDPI
Biosensors, Volume 6; https://doi.org/10.3390/bios6040058

Abstract:
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.
Kuang-Hsuan Chen, Yu-Wei Hsu, Jing-Jung Yang,
Instrumentation Science & Technology, Volume 45, pp 382-391; https://doi.org/10.1080/10739149.2016.1268155

Abstract:
Falling is a common accident that can lead to serious injury among the elderly. To reduce injuries resulting from falls, automatic detection allows immediate medical assistance. Hence, various fall detectors, including body sensors and smartphones, have been developed in recent decades. In our previous study, an accelerometer-based fall detector was designed with high accuracy for simulated falls performed by young volunteers. However, there are significant differences between the acceleration signals generated by simulated and real falls. Simulated devices do not accurately assess the sensitivity and specificity of falls. Hence, the goal of this study is to access the accuracy of our designed accelerometer-based fall detection algorithm using a real-world repository. The results showed that the algorithm accurately characterizes real falls. Differences between our approach and previously published algorithms are discussed. This study is expected to assist in the design of more effective practical fall detection algorithms.
Taro Nakano, , Steven Zupancic, Amanda Rodriguez, D.Y.C. Lie, J. Lopez, Tam Q. Nguyen
2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) pp 1-6; https://doi.org/10.1109/icis.2016.7550922

Abstract:
Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, we took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, we used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, we should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.
Kuang-Hsuan Chen, Jing-Jung Yang,
Instrumentation Science & Technology, Volume 44, pp 333-342; https://doi.org/10.1080/10739149.2015.1123161

Abstract:
Falls by the elderly may result in hip fractures, paraplegia, and even death. Hence, over the past few decades, considerable research has been conducted on fall detection. Here, an accelerometer-based fall detector is reported that is fastened to a person's waist and includes an accelerometer, a multiplexer, a fifth-order low-pass Butterworth filter, and a microcontroller. Acceleration sensing, noise filtering, and analog-to-digital conversion were performed by the circuitry. The processed signal was sent to a personal computer through Bluetooth and analyzed by customized software. The fall detection algorithm included feature extraction and a support vector machine algorithm for classifying the features. Twenty volunteers performed 12 trials of 6 daily activities and 6 fall events. The results show that the algorithm had high sensitivity (95%) and specificity (96.7%). Thus, this device is expected to have significant application for fall detection.
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