Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis

Mosquera-Lopez, Clara; Wan, Eric A.; Shastry, Mahesh C.; Folsom, Jonathon; Leitschuh, Joseph; Condon, John P; Rajhbeharrysingh, Uma; Hildebrand, Andrea; Cameron, Michelle; Jacobs, Peter G.

doi:10.1109/jbhi.2020.3041035

Cited by 28 publications

(25 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Publicly available datasets gathered from PwMS are largely related to medical imaging [66][67][68] and medication [69]. One dataset tackles a related issue: remote fall detection in PwMS [70], however, it is lacking data from PwMS who have yet to become recurrent fallers, preventing the investigation of gait as it relates to distinguishing fallers from non-fallers and potentially fall-risk prediction. Utilizing the presented data, potentially with other collected or open-source data, researchers may be able to leverage deep learning to enhance the performance of their digital biomarkers and phenotypes, and particularly for detecting fall risk in PwMS in both lab and free-living environments.…”

Section: Plos Digital Healthmentioning

confidence: 99%

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Meyer

Tulipani²,

Gurchiek³

et al. 2022

PLOS Digit Health

View full text Add to dashboard Cite

Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.

show abstract

Section: Plos Digital Healthmentioning

confidence: 99%

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Meyer

Tulipani²,

Gurchiek³

et al. 2022

PLOS Digit Health

View full text Add to dashboard Cite

show abstract

“…28 An automated and/or real-time fall detection system may help mitigate fall-counting errors in future studies. 29…”

Section: Discussionmentioning

confidence: 99%

Free From Falls education and exercise program for reducing falls in people with multiple sclerosis: A randomized controlled trial

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: People with multiple sclerosis (PwMS) fall frequently. Community-delivered exercise and education reduce falls in older adults, but their efficacy in multiple sclerosis (MS) is unknown. Objectives: To evaluate the impact of the Free From Falls (FFF) group education and exercise program on falls in PwMS. Methods: This was a prospective, assessor-blinded, two-arm parallel randomized controlled trial. Ninety-six participants were randomized to FFF (eight weekly 2 hour sessions) or the control condition (a fall prevention brochure and informing their neurologist of their fall history). Participants counted falls prospectively from enrollment through 6 months following intervention. Effects on fall frequency were evaluated by the Bayesian analysis. Results: The modeled mean fall frequency pre-intervention was 1.2 falls/month in the FFF group (95% credible intervals (CIs) = 0.8–2.0) and 1.4 falls/month in the control group (95% CI = 0.9–2.1). Fall frequency decreased by 0.6 falls/month in both groups over time (nadir 4–6 months post-intervention: FFF 0.6 falls/month (95% CI = 0.4–0.9); control 0.8 falls/month (95% CI = 0.5–1.1)). Conclusion: In-person group exercise and education are not superior to written education and neurologist-initiated interventions for preventing falls in PwMS.

show abstract

“…Some of the research indicates that the patterns of falls experienced by elderly people is similar to simulated falls from young subjects [ 45 , 46 ]. Acquiring data from elderly people that experience falls in real life situations is challenging, and only a few researchers have worked to acquire them [ 26 , 34 , 47 , 48 , 49 ]. Those acquired datasets are not publicly available.…”

Section: Discussionmentioning

confidence: 99%

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Šeketa

Pavlaković

Džaja

et al. 2021

Sensors

View full text Add to dashboard Cite

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

show abstract

Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis

Cited by 28 publications

References 48 publications

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis

Free From Falls education and exercise program for reducing falls in people with multiple sclerosis: A randomized controlled trial

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Contact Info

Product

Resources

About