A multi-sensor approach for fall risk prediction and prevention in elderly

Majumder, Akm Jahangir Alam; Zerin, Ishmat; Ahamed, Sheikh Iqbal; Smith, Roger O.

doi:10.1145/2600617.2600621

Cited by 29 publications

(29 citation statements)

References 17 publications

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“…In this sense, it is still under discussion if the signals captured from healthy young adults who emulate a fall (or athletes who are skilled to cushion hits provoked by falls) can be extrapolated to the mobility patterns that older people actually present during a fall [ 33 ]. Authors in [ 41 ] have shown that the effectiveness of mobility pattern classifiers clearly plummets when the recognition of the dynamics of a certain individual is founded on other subject’s gait. On the contrary, Kangas et al compared in [ 5 ] the acceleration signals of intentional falls and those captured from actual falls suffered by long-term monitored elderly, concluding that they exhibit a similar profile.…”

Section: Analysis Of the Testbed: Experimental Subjects And Scenarmentioning

confidence: 99%

Analysis of Public Datasets for Wearable Fall Detection Systems

Casilari

Santoyo-Ramón

Cano-García

2017

Sensors

115

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Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

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Section: Analysis Of the Testbed: Experimental Subjects And Scenarmentioning

confidence: 99%

Analysis of Public Datasets for Wearable Fall Detection Systems

Casilari

Santoyo-Ramón

Cano-García

2017

Sensors

115

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“…In this context, Bluetooth 2.0 is employed in the hybrid prototype presented in [58] but authors state that Bluetooth 2.0 connections deplete the 600 mAh battery of the external mote in less than 4 h. In [45], Hou et al estimate a battery lifetime of only 7 h in a HTC terminal when the mobile is utilized to receive the data via Bluetooth from an external accelerometer and decide if a fall has occurred. Majumder et al present an architecture [76] that integrates a smartphone (an iPhone model) and a smartshoe (containing four pressure sensors) that intercommunicate via Wi-Fi. The tests applied to the iPhone model reveal that the detection application reduces the battery lifespan to less than 3 h.…”

Section: Consumption In Hybrid Fall Detection Architectures (Combininmentioning

confidence: 99%

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

González-Cañete

Casilari

2020

Sensors

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Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

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“…Wireless systems integrated into shoes provide possibilities for assessing fall risk factors from gait and assessing the critical weather conditions affecting the fall risk [39][40][41][42][43] to distinguish between normal and abnormal walking patterns [42].…”

Section: Wearable Sensorsmentioning

confidence: 99%

Technologies for fall risk assessment and conceptual design in personal health record system

et al. 2019

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Falls among older people are a major economic and public health problem. Due to the demographic change and aging of populations, there is an urgent need for accurate screening tools to identify those at risk to target effective falls prevention strategies. Clinical fall risk assessments are costly and time-consuming and thus cannot be performed frequently. Technologies provide means for assessing fall risk during daily living, making self-evaluations and fast methods for fall risk assessment for professional use. This study collects and evaluates existing technological solutions for fall risk assessment including various different sensor technologies. The study also presents one easy to use solution for assessing fall risk and suggests a concept-design for integrating sensor-based solutions into the Finnish national Kanta Personal Health Record. The optimal solution for technological fall risk assessment is still unclear. A wide implementation still requires extensive validation studies, adoption to health care processes and novel IoT -solutions for collecting large amounts of sensor data. Thorough methods should be utilised in designing the privacy and security aspects of fall risk assessment solutions, as well as different user profiles, to allow suitable interfaces and visualisations to users. It should always be clear what kind of data are collected from users and how the data are utilised. The consent of the users should also always be collected.

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A multi-sensor approach for fall risk prediction and prevention in elderly

Cited by 29 publications

References 17 publications

Analysis of Public Datasets for Wearable Fall Detection Systems

Analysis of Public Datasets for Wearable Fall Detection Systems

Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Technologies for fall risk assessment and conceptual design in personal health record system

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