Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Rucco, Rosaria; Sorriso, Antonietta; Liparoti, Marianna; Ferraioli, Giampaolo; Sorrentino, Pierpaolo; Ambrosanio, Michele; Baselice, Fabio

doi:10.3390/s18051613

Cited by 97 publications

(126 citation statements)

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“…Rucco et al [10] provided a review of the approaches that have been proposed for fall assessment, fall prevention, and fall detection that use wearable devices. According to their survey, most of the wearable-device-based methods adopted triaxial accelerometers to collect fall signals.…”

Section: Wearable-device-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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Section: Wearable-device-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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“…Poor balance usually affects performance in motor activities of daily living and is also one of the major factors in the risk of a fall [1,2,3]. Although stable control of posture and balance is automatic for people under normal conditions, it is a challenge for persons with vestibular deficiency, children with cerebral palsy, the elderly, and others who lack stability due to pathologies, deformities or injuries [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Trauma resulting from falls, especially in elderly people who live most of their daily life out of contact with the rest of society, are the most significant causes of fractures, injuries and deaths [5]. Falls can cost approximately 10 billion dollars annually worldwide [7,6]. Elderly residents in long-term care homes fall for a variety of reasons and are more likely to suffer injuries due to falls than those living in the community [8].…”

Section: Introductionmentioning

confidence: 99%

“…There are also risks associated not only with falls, but also with other factors related to the loss of stability in people with a balance problem, e.g., persons with a neurological disorder, physical deformity or arthritis [11,6]. Such risks involve a reduction in the quality of life due to an excessively cautious gait, instability, excessive fear of falling, and abnormal postural responses [3,6]. These risks result in secondary characteristics, such as depressive symptoms, increased anxiety, mild extrapyramidal symptoms, and mild cognitive decline [12,11].…”

Section: Introductionmentioning

confidence: 99%

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System for the Analysis of Human Balance based on Accelerometers and Support Vector Machines

Pinheiro

Carmo

Miosso

2020

Preprint

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Background: Disturbances in balance control lead to movement impairment and severe discomfort, dizziness, and vertigo. They can also lead to serious accidents, due to the loss of balance in critical conditions. It is important to monitor the level of balance in order to determine the risk of a fall and to evaluate progress during treatment. Some solutions exist, such as those based on cameras and force platforms, but they are generally restricted to indoor environments. We propose and evaluate a system, based on accelerometers and support vector machines (SVMs), that indicates the user’s postural balance variation by monitoring signals related to balance, and which can be used in indoor and outdoor environments. Results and Discussion: The results show that the system can classify the acquired signals into two to seven levels of balance, with success rates ranging from 92.5% (for seven levels) to 98.3%, in 1000 sessions of random resampling. With two levels of balance, the system attains in the best case an accuracy of 98.9%. The average accuracy with two levels of balance was signiﬁcantly greater than 93% (p=0.045) and the accuracy was signiﬁcantly greater than 97% (p=0.044). With seven levels of balance, the accuracy was signiﬁcantly greater than 94% (p=0.046) and the precision was signiﬁcantly greater than 80% (p=0.049). The tests performed with the dolls show that the system is able to distinguish between the conditions of a sudden drop and of a recovery of balance after losing one’s balance. In this case, the average accuracy was greater than 95% (p=0.043) and the precision was greater than 95% (p=0.026). The system was also able to infer the centroid of each COP region with an error lower than 0.9cm (p=0.0045). These results suggest that the system can be used to detect variations in balance and, therefore, to indicate the risk of a fall even in outdoor environments. Methods: The proposed system consists of a second-skin shirt, six accelerometers, a 328 ATMEGA microcontroller, and a local storage module. For the training phase, we used the accelerometer signals acquired from a single subject under monitored conditions of balance and intentional imbalance, and used the scores provided by a validated commercial solution (the SWAY® software) for establishing the reference target values. Based on these targets, we trained an SVM to classify the signal into n levels of balance (with n varying from 2 to 7) and later evaluated the performance using cross validation by random resampling. We also developed an SVM approach for estimating the center of pressure based on the signals from the accelerometers, by using as reference targets the results from a force platform by AMTI®. We considered ﬁve possible regions for the center of mass, and our system was used to determine the correct region using the accelerometer signals. For validation, we performed experiments with a subject who was ﬁrst standing, and later walking, performing a body rotation, and performing sudden intentional drops. Later the subject was requested to stand and then incline in four main directions, so the diﬀerent centers of pressure (COPs) could be computed by our system and compared to the results from the force platform. We also performed tests with a dummy and a John Doe doll, in order to observe the system’s behavior in the presence of a sudden drop or a lack of balance.

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“…It is an information processing system based on imitating the structure and function of the brain neural network [37]. The theory of an ANN has made significant progress in pattern recognition [38,39], automatic control, signal processing [40], assistant decision-making, artificial intelligence [41], networking and healthcare [42][43][44][45]. It has been successfully introduced into the field of hydrology and water resources [46,47].…”

Section: Introductionmentioning

confidence: 99%

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

Wang

Xia

2019

Applied Sciences

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Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.

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Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

Cited by 97 publications

References 98 publications

An Image-Based Fall Detection System for the Elderly

An Image-Based Fall Detection System for the Elderly

System for the Analysis of Human Balance based on Accelerometers and Support Vector Machines

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

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