Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Silva, Joana; Sousa, Inês; Cardoso, Jaime S.

doi:10.1109/jbhi.2019.2951230

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…One limitation was the impracticality of experimental blinding of the participants. Another possible limitation lies in the fact that the baseline history of falls was based on self-report[ 57 , 58 ], in contrast to the prospective data collection. In addition, the final sample size was smaller than recruited and calculated (n=30), so must be considered as a limitation.…”

Section: Discussionmentioning

confidence: 99%

Balance Exercise Circuit for fall prevention in older adults: a randomized controlled crossover trial

Costa¹,

Ribeiro²,

Ribeiro³

et al. 2022

JFSF

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Objectives: This study aimed to assess the immediate and short-term effects of the Balance Exercise Circuit (BEC) on muscle strength, postural balance, and quality of life, with the aim of preventing falls in older adults. Methods: Twenty-two volunteers participated in this randomized controlled crossover study. Group A performed BEC training in the initial 3 months and received no intervention in the following 3 months. Group B received no intervention during the first 3 months and then participated in BEC training for the next 3 months. In addition, participants were followed for an additional 3 months. Muscle strength, postural balance, functional mobility, and quality of life were assessed, respectively, using an isokinetic dynamometer, force platform, TUG test, and the WHOQOL. Results: After 3 months of training, Group A presented improved balance and rate of force development (RFD), while Group B presented improvements in RFD, TUG performance, and WHOQOL physical and psychological domains. Regarding the short-term effects, the participants maintained the training effects in WHOQOL balance, RFD, and the social domain. In addition, the number of falls decreased during follow-up. Conclusion: The BEC intervention improved muscle strength, postural balance, and quality of life in older adults, in addition to reducing the risk of falls. Trial registration: Brazilian Registry of Clinical Trials (ReBEC) - RBR-5nvrwm.

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Section: Discussionmentioning

confidence: 99%

Balance Exercise Circuit for fall prevention in older adults: a randomized controlled crossover trial

Costa¹,

Ribeiro²,

Ribeiro³

et al. 2022

JFSF

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“…In addition to the variables retrieved by the clinical application (eg, personal profile, medical conditions, medication, answers to the questionnaires, and scores of the 3 functional tests, the home application will allow the measurement of the range of motion along with the number of repetitions and durations of ascending and descending movements for the 8 exercises of the OEP, namely, knee flexion, knee extension, hip abduction, knee bending, toe raises, calf raises, sit-to-stand, and one-leg standing exercises. Previous studies have set a background for the technological solutions used in this study [45,46]. This study has some limitations, namely, the use of a nonrandom sample and the absence of a control group.…”

Section: Discussionmentioning

confidence: 99%

A Technological-Based Platform for Risk Assessment, Detection, and Prevention of Falls Among Home-Dwelling Older Adults: Protocol for a Quasi-Experimental Study

Araújo¹,

Nogueira²,

Silva³

et al. 2021

JMIR Res Protoc

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Background According to the United Nations, it is estimated that by 2050, the number of people aged 80 years and older will have increased by 3 times. Increased longevity is often accompanied by structural and functional changes that occur throughout an individual’s lifespan. These changes are often aggravated by chronic comorbidities, adopted behaviors or lifestyles, and environmental exposure, among other factors. Some of the related outcomes are loss of muscle strength, decreased balance control, and mobility impairments, which are strongly associated with the occurrence of falls in the elderly. Despite the continued undervaluation of the importance of knowledge on fall prevention among the elderly population by primary care health professionals, several evidence-based (single or multifaceted) fall prevention programs such as the Otago Exercise Program (OEP) have demonstrated a significant reduction in the risk of falls and fall-related injuries in the elderly within community settings. Recent studies have strived to integrate technology into physical exercise programs, which is effective for adherence and overcoming barriers to exercise, as well as improving physical functioning. Objective This study aims to assess the impact of the OEP on the functionality of home-dwelling elderly using a common technological platform. Particularly, the impact on muscle strength, balance, mobility, risk of falling, the perception of fear of falling, and the perception of the elderly regarding the ease of use of technology are being examined in this study. Methods A quasi-experimental study (before and after; single group) will be conducted with male and female participants aged 65 years or older living at home in the district of Porto. Participants will be recruited through the network COLABORAR, with a minimum of 30 participants meeting the study inclusion and exclusion criteria. All participants will sign informed consent forms. The data collection instrument consists of sociodemographic and clinical variables (self-reported), functional evaluation variables, and environmental risk variables. The data collection tool integrates primary and secondary outcome variables. The primary outcome is gait (timed-up and go test; normal step). The secondary outcome variables are lower limb strength and muscle resistance (30-second chair stand test), balance (4-stage balance test), frequency of falls, functional capacity (Lawton and Brody - Portuguese version), fear of falling (Falls Efficacy Scale International - Portuguese version), usability of the technology (System Usability Scale - Portuguese version), and environmental risk variables (home fall prevention checklist for older adults). Technological solutions, such as the FallSensing Home application and Kallisto wearable device, will be used, which will allow the detection and prevention of falls. The intervention is characterized by conducting the OEP through a common technological platform 3 times a week for 8 weeks. Throughout these weeks, the participants will be followed up in person or by telephone contact by the rehabilitation nurse. Considering the COVID-19 outbreak, all guidelines from the National Health Service will be followed. The project was funded by InnoStars, in collaboration with the Local EIT Health Regional Innovation Scheme Hub of the University of Porto. Results This study was approved on October 9, 2020 by the Ethics Committee of Escola Superior de Enfermagem do Porto (ESEP). The recruitment process was meant to start in October, but due to the COVID-19 pandemic, it was suspended. We expect to restart the study by the beginning of the third quarter of 2021. Conclusions The findings of this study protocol will contribute to the design and development of future robust studies for technological tests in a clinical context. Trial Registration ISRCTN 15895163; https://www.isrctn.com/ISRCTN15895163 International Registered Report Identifier (IRRID) PRR1-10.2196/25781

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“…Sensor fusion is a common approach to complementing sensor modalities for HIAR [ 1 ]. Bazo et al proposed a sensor fusion model, Baptizo, in [ 43 ] that it leverages active RF positioning data captured with ultra-wideband (UWB) devices and RGB-Depth (RGBD) human pose estimation for the reduction of human positioning error to assist with the eventual activity recognition classification.…”

Section: Related Workmentioning

confidence: 99%

“…Towards this end, the field of human identification and activity recognition (HIAR) can be presented. HIAR has applications in a variety of medical domains, including elderly monitoring [ 1 , 2 , 3 ], smart living [ 4 ], and medical care [ 5 ]. Currently, HIAR technologies are primarily deployed via computer vision [ 6 ], wearable sensors [ 7 ], and ambient sensing [ 8 ] methods.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Yuan

Andrews

et al. 2022

Sensors

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Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.

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Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Cited by 9 publications

References 15 publications

Balance Exercise Circuit for fall prevention in older adults: a randomized controlled crossover trial

Balance Exercise Circuit for fall prevention in older adults: a randomized controlled crossover trial

A Technological-Based Platform for Risk Assessment, Detection, and Prevention of Falls Among Home-Dwelling Older Adults: Protocol for a Quasi-Experimental Study

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

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