Online stability estimation based on inertial sensor data for human and humanoid fall prevention

Steffan, Laura; Kaul, Lukas; Asfour, Tamim

doi:10.1109/humanoids.2017.8239553

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…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%

“…Fall Detection [104], [153] 5% Fall Prevention [147], [150], [151], [130] 9% Fitness Monitoring [162], [136] 5% FoG Detection [142], [108], [167] 7% Gait Classification [162], [116] 5% Gait Improvement [86], [168], [136] 7% HAR [92], [96], [119], [150], [131], [89], [105], [133] 18% Injury Avoidance [159], [ As mentioned in Section III, these applications are subdivided into specific groups such as HAR, disease diagnosis, gait classification, and injury avoidance. TABLE 6 lists the publications according to their applicability and percentage of papers.…”

Section: Applicationmentioning

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: Papermentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

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

et al. 2020

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“…Apparently, machine learning techniques can also be used to predict falls, for example, SVM-based preimpact fall detector was proposed by Zhen et al [148] and Aziz et al [152]. Neural networks were constructed by Steffan et al [150] to prevent falls. Certainly, there are many other machine learning-based fall detection and fall prevention methods which have not been mentioned in this paper.…”

Section: ) Non-threshold-based Fall Detection and Fall Preventionmentioning

confidence: 99%

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

2019

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Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field. INDEX TERMSAdaptive algorithm, classification algorithms, fall detection, fall prevention, low power techniques, sensing techniques. LINGMEI REN received the Ph.D. degree from the

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“…In similar studies, SVMbased [33,34] pre-impact fall event detection systems were introduced. In another study, neural networks were developed [35] to avoid fall events. ese studies have illustrated that the accuracy of machine learning-based fall detection systems is greater than threshold-based systems due to trained classifiers on extracted features.…”

Section: Related Workmentioning

confidence: 99%

Falling and Drowning Detection Framework Using Smartphone Sensors

Alqahtani

Alsubai

Sha

et al. 2022

Computational Intelligence and Neuroscience

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Advancements in health monitoring using smartphone sensor technologies have made it possible to quantify the functional performance and deviations in an individual’s routine. Falling and drowning are significant unnatural causes of silent accidental deaths, which require an ambient approach to be detected. This paper presents the novel ambient assistive framework Falling and Drowning Detection (FaDD) for falling and drowning detection. FaDD perceives input from smartphone sensors, such as accelerometer, gyroscope, magnetometer, and GPS, that provide accurate readings of the movement of an individual’s body. FaDD hierarchically recognizes the falling and drowning actions by applying the machine learning model. The approach activates embedding, in a smartphone application, to notify emergency alerts to various stakeholders (i.e., guardian, rescue, and close circle community) about drowning of an individual. FaDD detects falling, drowning, and routine actions with good accuracy of 98%. Furthermore, the FaDD framework enhances coordination to provide more efficient and reliable healthcare services to people.

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Online stability estimation based on inertial sensor data for human and humanoid fall prevention

Cited by 10 publications

References 25 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

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Falling and Drowning Detection Framework Using Smartphone Sensors

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