A pervasive solution for risk awareness in the context of fall prevention

Caporusso, Nicholas; Lasorsa, I.; Rinaldi, Oliviero; Pietra, Leonardo La

doi:10.4108/icst.pervasivehealth2009.5980

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…A concept introduced in [ 8 ], not implemented though, proposes the addition of localization features to a fall prevention system. The idea is to assess the risk of a fall depending on the current location of the subject.…”

Section: Fall Prevention Systemsmentioning

confidence: 99%

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Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

DeLaHoz

Labrador

2014

Sensors

268

128

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According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

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Section: Fall Prevention Systemsmentioning

confidence: 99%

“…The acceleration is biased to its negative component along the axis perpendicular to the ground. There is also a change in the direction after a fall followed by a period of the subject's inactivity [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

DeLaHoz

Labrador

2014

Sensors

268

128

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“…Fallarm [36] Inertial sensor Wrist Traffic light-like alert system Low NA iCane [37] Force sensor + laser rangefinders Detailed and correct functional assessment is the basis of formulating a therapeutic schedule [4]; therefore, further detailed identification concerning falls and abnormal gaits is required. The second SVM-RBF-KNN classifier is intended to classify specific types of falls or abnormal gaits in a supervised manner.…”

Section: Sensitivitymentioning

confidence: 99%

A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot

Yang

Gao

et al. 2019

Applied Sciences

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Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types.

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“…The challenges include ensuring the privacy of patients' data, data protection, management of sensed data, support for patient mobility, wearable systems -low-power operation. Fall detection, prevention and reaction for elderly or disabled people [48]. The challenges include location-awareness, data processing complexity for fall detection, mobile and nomadic operations, reliability requirements.…”

Section: Domotics and Home Automationmentioning

confidence: 99%

“…The first fragment is routed using the IPv6 Forwarding method, and a state is installed for the [48]. Figure 8 shows the three forwarding techniques supported by the 6TiSCH architecture applied to the track installed between the node A and node U in Fig.…”

Section: Fragmentmentioning

confidence: 99%

Internet of Things

2014

Smart Sensors, Measurement and Instrumentation

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A pervasive solution for risk awareness in the context of fall prevention

Cited by 10 publications

References 12 publications

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot

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