Wearable Inertial Sensing for ICT Management of Fall Detection, Fall Prevention, and Assessment in Elderly

Genovese, V.; Mannini, Andrea; Guaitolini, M.; Sabatini, A.M.

doi:10.3390/technologies6040091

Cited by 13 publications

(15 citation statements)

References 32 publications

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“…Future work on fall detection devices can incorporate SRS for identifying joint kinematics. Moreover, wireless sensor networks, algorithms, and machine learning techniques have been used along with accelerometers and IMUs for fall detection [6,[8][9][10]39] and in the future can also be implemented using SRS. However, adding electromyography (EMG) for fall detection in addition to joint kinematics detection can increase the accuracy of pre-impact fall detection, using both biomechanical and neuromuscular measures.…”

Section: Future Workmentioning

confidence: 99%

“…However, adding electromyography (EMG) for fall detection in addition to joint kinematics detection can increase the accuracy of pre-impact fall detection, using both biomechanical and neuromuscular measures. The concept of pre-impact fall detection has been suggested earlier in attempts for early fall detection by using inertial sensors and fall-threshold-detecting algorithms [6,7,22,40]. Pre-impact fall detection research has been successful in detecting fall events at least 70 ms before the impact with the ground [7] and with an average lead time of 700 ms before the impact occurs, with no false alarms [40].…”

Section: Future Workmentioning

confidence: 99%

“…A hierarchy of approaches for fall detection has been previously proposed that includes camera-based systems to assess change in body shape, inactivity detection or three-dimensional (3D) head motion analysis, an ambience device that determines posture and presence, and wearable devices that evaluate posture and motion [5]. However, the camera-based systems and ambient device systems have their own limitations [6] such as capture obstruction, privacy concerns, false alarms, battery life, and sole intended use of the device [7,8]. Previous literature has reported that wearable devices can successfully detect induced falls in a laboratory setting [21] or other indoor environments [8].…”

Section: Introductionmentioning

confidence: 99%

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Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

et al. 2019

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Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.

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Section: Future Workmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

et al. 2019

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“…Genovese et al [20] introduced fall prevention and detection method through a waist-worn device. A wearable inertial measurement unit (WIMU) was attached at the subject's waist, where a data logger was maintained to not only to detect falls but also reduce its risk.…”

Section: Introductionmentioning

confidence: 99%

Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features

et al. 2021

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Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We have also experimented with the proposed pipeline's efficacy by training and testing it with different datasets. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.

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“…Another branch of eHealth is the monitoring of elderly people to early detect symptoms related to possible health treats (e.g., frailty, falls, dementia, etc.). In this context, Genovese et al in [6] reports the sensor description and the preliminary testing of a an integrated fall detection and prevention ICT service for elderly people based on wearable smart sensors. Falls are one of the most common causes of accidental injury: approximately, 37.3 million falls requiring medical intervention occur each year.…”

mentioning

confidence: 99%

Special Issue on “Wearable Technologies”

Tognetti

Carbonaro

2018

Technologies

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Wearable Inertial Sensing for ICT Management of Fall Detection, Fall Prevention, and Assessment in Elderly

Cited by 13 publications

References 32 publications

Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features

Special Issue on “Wearable Technologies”

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