Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement

Kostopoulos, Panagiotis; Nunes, Tiago; Salvi, Kevin; Deriaz, Michel; Torrent, Julien

doi:10.5220/0005179100300036

Cited by 20 publications

(12 citation statements)

References 13 publications

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“…The work presented here is based on experiments recorded on real cases of falls [2] and completes previous studies [3] that used TBM (figure 1) to distinguish between hard falls (falling from the standing position) and normal activities (walking, sitting,climbing steps, freestyle). Other research team obtained similar results on hard falls like Kau & al.…”

Section: Introductionmentioning

confidence: 84%

Soft Fall Detection Using Machine Learning in Wearable Devices

Genoud

Cuendet

Torrent

2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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Wearable watches provide very useful linear acceleration information that can be use to detect falls. However falls not from a standing position are difficult to spot among other normal activities. This paper describes methods, based on pattern recognition using machine learning, to improve the detection of "soft falls". The values of the linear accelerometers are combined in a robust vector that will be presented as input to the algorithms. The performance of these different machine learning algorithms is discussed and then, based on the best scoring method, the size of the time window fed to the system is studied. The best experiments lead to results showing more than 0.9 AUC on a real dataset. In a second part, a prototype implementation on an Android platform using the best results obtained during the experiments is described.

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Section: Introductionmentioning

confidence: 84%

Soft Fall Detection Using Machine Learning in Wearable Devices

Genoud

Cuendet

Torrent

2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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“…The first one is the scenario of the user being outdoors. Then, the fall detection system will work without any further filtering [21], minimizing the probability that we miss any fall that can be dangerous for the end-user.…”

Section: ) Locationmentioning

confidence: 99%

“…If (counter ≥ Y ), then it is due to another activity being performed (e.g., running) which gives the difference in the acceleration values as we can see in Figure 6. Based on the real ADL data that we processed from elderly people we concluded that the value Y that gives the best specificity lays between [5][6][7][8][9][10] and not 14 comparing to [21]. On the other hand, if (counter < X) where X = 1 it means the user at most did a sudden movement with his wrist and so the threshold conditions were not satisfied (e.g., when a user was going down the stairs in Figure 8).…”

Section: B Fall Detection Algorithmmentioning

confidence: 99%

“…The time interval between the higher and the lower peak is flexible but cannot exceed the 0.5 seconds which is the maximum length of the rebound after a fall according to our experiments. Making the time interval flexible instead of giving it fixed values like in our previous work[21] has led to an increase of the specificity of the algorithm of 3%. The ranges of the two thresholds have been selected based on the basic trade-off between detecting all falls and avoiding false positives.…”

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confidence: 97%

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F2D: A fall detection system tested with real data from daily life of elderly people

Kostopoulos

Nunes

Salvi

et al. 2015

2015 17th International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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Falls among older people remain a very important public healthcare issue. Every year over 11 million falls are registered in the U.S. alone. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The final decision of a fall event is taken based on the location of the user. To the best of our knowledge, this is the first fall detection system which works on an independent smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By analyzing real data of activities of daily life of elderly people, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account the residual movement and the location of the user to increase the fall detection accuracy. By testing with real data we have a fall detection system ready to be deployed on the market.

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“…In addition, the development of Micro Electro Mechanical Systems (MEMS) allows for such sensors to be worn directly in various places on the human body, with data collected, processed and transmitted in real time anywhere and anytime. Acceleration sensors are now widely used in various types of human motion recognition research, including common daily motion activities such as walking [10], running [11], and ascending/descending stairs [12]. Real-time human motion recognition is useful for fall detection in home care research [13].…”

Section: Introductionmentioning

confidence: 99%

A Brunnstrom Stage Evaluation System with Fuzzy-Accelerometer

Tsai

et al. 2019

Applied Sciences

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Advances in medical care has reduced the rate of mortality from strokes, but the incidence of stroke has remained stable while the incidence of ministrokes has increased. Most stroke victims require long-term care, imposing a heavy financial and emotional burden on families while incurring a heavy cost to society. Thus, strokes are a key issue in the context of health care in Taiwan. This paper proposes using VBA (Visual Basic for Applications) to build a system for assessing Brunnstrom stages based on the observation of several obvious rehabilitation features The system calculates features for accelerometer readings, which are then used as input parameters for a fuzzy algorithm to obtain the Brunnstrom action level. Experimental results show the proposed approach effectively assesses Brunnstrom level, and that the approach can be used to assist physical therapists in performing longitudinal assessments of stroke victim progress, thus improving evaluation efficiency.

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Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement

Cited by 20 publications

References 13 publications

Soft Fall Detection Using Machine Learning in Wearable Devices

Soft Fall Detection Using Machine Learning in Wearable Devices

F2D: A fall detection system tested with real data from daily life of elderly people

A Brunnstrom Stage Evaluation System with Fuzzy-Accelerometer

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