Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements

Sabatini, A.M.; Ligorio, Gabriele; Mannini, Andrea; Genovese, V.; Pinna, L.

doi:10.1109/tnsre.2015.2460373

Cited by 68 publications

(39 citation statements)

References 36 publications

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“…Absolute accuracy not achieved due to difficulties found to detect falls with rotation movement Sabatini et al [153] Wearable (inertial sensors and barometric altimeter) worn on the waist which linked with a smartphone (Samsung Galaxy SII, GT-I9100) via Bluetooth. Scripted falls and activities.…”

Section: Younger Adultsmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) are one approach to offer a viable means for ubiquitous, sustainable and scalable monitoring in habitual free-living environments. Gait has been presented as a relevant (bio) marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of recent wearable gait assessment literature, focusing on the need for an algorithm fusion approach to measurement cumulating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Opportunities for how this domain needs to progress are also summarised. Highlights• Wearables can meet older adults' needs for independent living.• Gait assessment is a (bio)marker within ageing and different pathologies.• Measuring gait with wearables has been innovative but fraught with inconsistencies.

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Section: Younger Adultsmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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“…When the user lay on the ground, the system triggered an alarm. Pierlenoi [16] and Sabatini et al [17] used not only triaxial accelerometers but also gyroscope, magnetometer, and barometer sensors to recognize the posture of users. Ejupi et al [18] developed a method to identify users at risk of falls and maintain daily tracking.…”

Section: Wearable-device-based Methodsmentioning

confidence: 99%

“…As mentioned above, the height ratio of the tester to the chair for Figure 10 is 1.74, and the height ratio of the tester to the chair for Figure 12 is 2.12. In all test cases, since IFADS detects the change in the person's state, it can detect correctly even if the tester cannot be detected, because the human features of the falling tester cannot be found in Cases 14,15,16,17,20,21,22, and 23. In Cases 14,15,16,21,22, and 23, IFADS cannot detect the testers, because some of the testers' features are obscured by the chair.…”

Section: Redmentioning

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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“…Fall prevention is intended in this context to provide support to clinical decisions regarding the fall risk profile of patients, whose gait is assessed using wearable sensor technologies. In regard to the problem of fall detection, we have previously developed a method for pre-impact fall detection based on sensor fusion algorithms combining data from a waist-based inertial measurement unit integrating an air pressure sensor, [23]. There, a thorough discussion of the advantages of using air pressure sensors in a fall detector was done, and our results were compared to those achieved by alternative implementations of state-of-the-art waist-based fall detectors.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Falls are one of the most common causes of accidental injury: approximately, 37.3 million falls requiring medical intervention occur each year. Fall-related injuries may cause disabilities, and in some extreme cases, premature death among older adults, which has a significant impact on health and social care services. In recent years, information and communication technologies (ICT) have helped enhance the autonomy and quality of life of elderly people, and significantly reduced the costs associated with elderly care. We designed and developed an integrated fall detection and prevention ICT service for elderly people, which was based on two wearable smart sensors, called, respectively, WIMU fall detector and WIMU data-logger (Wearable Inertial Measurement Unit, WIMU); their goal was either to detect falls and promptly react in case of fall events, or to quantify fall risk instrumentally. The WIMU fall detector is intended to be worn at the waist level for use during activities of daily living; the WIMU logger is intended for the quantitative assessment of tested individuals during the execution of clinical tests. Both devices provide their service in conjunction with an Android mobile device. The ICT service was developed and tested within the European project I-DONT-FALL (Integrated prevention and Detection sOlutioNs Tailored to the population and risk factors associated with FALLs, funded by EU, action EU CIP-ICT-PSP-2011-5: GA #CIP-297225). Sensor description and preliminary testing results are provided in this paper.

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Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements

Cited by 68 publications

References 36 publications

Wearables for independent living in older adults: Gait and falls

Wearables for independent living in older adults: Gait and falls

An Image-Based Fall Detection System for the Elderly

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

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