Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention

Hsieh, Chia-Yeh; Shi, Wan-Ting; Huang, Hsiang-Yun; Liu, Kai-Chun; Hsu, Steen J.; Chan, Chia-Tai

doi:10.1109/icasi.2018.8394388

Cited by 22 publications

(17 citation statements)

References 5 publications

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“…Hsieh et al [151]: This paper's aim is to identify the fall characteristics to develop a strategic plan for fall prevention. Participants perform seven different falls and six ADLs for data acquisition wearing an accelerometer.…”

Section: Limitationsmentioning

confidence: 99%

“…The majority of studies in our analysis lack a significant amount of participants. Hence, such studies use SVM due to its ability to work well in the presence of a small sample size [151], [159]. Lastly, the SVM is computationally less expensive and generates faster results as compared to deep learning approaches.…”

Section: ) Distribution Based On MLmentioning

confidence: 99%

“…Medical Fitness Security Wu et al [86] Chen et al [89] Zebin et al [92] Ordóñez et al [96] Neverova et al [101] Zhen et al [104] Chen et al [105] Camps et al [108] Gharani et al [109] McGinnis et al [111] Zhao et al [116] Murad et al [119] Dehzangi et al [128] Steffan et al [130] Almaslukh et al [131] Cheng et al [133] Zdravevski et al [136] Abdulhay et al [137] Gadaleta et al [139] Xia et al [142] Asuncion et al [144] Huang et al [146] Aicha et al [147] Rescio et al [150] Hsieh et al [151] Putra et al [153] Ghazali et al [154] Rastegari et al [155] Gurchiek et al [159] Zhang et al [162] Abujrida et al [165] Kim et al [167] Wang et al [168]…”

Section: Papermentioning

confidence: 99%

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Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Limitationsmentioning

confidence: 99%

Section: ) Distribution Based On MLmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

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Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…The proposed model in [18] has an accuracy 0.86, while the [25] model has an accuracy 0.96. Approach in [27] reached accuracy 0.97, while the [28] model achieved accuracy 0.89. Equivalently, we applied McNemar's test to evaluate the statistical significance of the prediction accuracies achieved by all models.…”

Section: Resultsmentioning

confidence: 97%

“…IoT and Bluetooth capabilities can enable wearable low power models for efficient pre-fall detection and prediction [26]. Machine learning can be also used to evaluate fall characteristics, which can support a monitoring system for the strategic plan and fall prediction of the elderly and impaired population [27].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

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SVM Time Series Classification of Selected Gait Abnormalities

Rostovski

Krivošei

Kuusik

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention

Cited by 22 publications

References 5 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

SVM Time Series Classification of Selected Gait Abnormalities

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