Challenges in Studying Falls of Community-Dwelling Older Adults in the Real World

Hu, Xin; Dor, Rahav; Bosch, Steven L.; Khoong, Anita; Li, Jing; Stark, Susan; Lu, Chenyang

doi:10.1109/smartcomp.2017.7946993

Cited by 6 publications

(17 citation statements)

References 8 publications

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“…Five articles did not provide any additional descriptive information on the participants [ 23 , 24 , 35 , 37 , 40 ]. The other eighteen articles describe participant’s age, twelve also provide gender information and six provide details of height and weight or BMI [ 17 , 25 , 29 , 31 , 32 , 34 ]. Four articles provided information on specific medical conditions, three recruited participants with Progressive Supranuclear Palsy [ 17 , 21 , 36 ] and one included a single older adult with Parkinson’s disease [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…Fifteen studies used wearable technology and ten used non-wearable, Table 2 shows full details of the devices used in each study. Accelerometers are the most common choice of sensor and have been used in 15 of the studies [ 17 , 21 , 22 , 23 , 24 , 25 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 38 , 40 ]. Eight studies tested some form of optical sensor [ 20 , 22 , 26 , 27 , 28 , 30 , 37 , 39 ], making them the most common choice of non-wearable devices.…”

Section: Resultsmentioning

confidence: 99%

“…Where reported, the length of the monitoring period varied considerably and comparison is made difficult by the inconsistent choice of reported metrics. Thirteen articles provided the total length of the recorded data, but did not provide details of the proportion where the system was recording participant’s movement (participant in the capture area or wearing the device) [ 20 , 21 , 22 , 24 , 26 , 27 , 28 , 30 , 32 , 34 , 35 , 38 , 39 ]. The median length of total recorded data, from studies which provided it, was 592 days (IQR 21–1474).…”

Section: Resultsmentioning

confidence: 99%

“…It was unclear what secondary method of fall identification was used in one of the studies [ 24 ]. Of the 18 studies which analysed the data retrospectively, three identified falls using staff reports [ 17 , 21 , 39 ], five used participant self-report [ 29 , 30 , 31 , 32 , 38 ] and ten did not state how falls were identified [ 20 , 23 , 25 , 26 , 27 , 28 , 35 , 36 , 37 , 40 ].…”

Section: Resultsmentioning

confidence: 99%

“…This could, for example, be due to delays in completing the report, delays in the faller being discovered, participant recall problems or staff naturally prioritising helping the faller over checking and reporting the time. Only three articles describe methods to check reported fall times [ 17 , 21 , 32 ]. Two of these [ 17 , 21 ] used datasets from the FARSEEING repository where expert analysis of the sensor signals in combination with fall reports was used to pinpoint the fall signal.…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

Broadley

Klenk

Thies

et al. 2018

Sensors

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Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

Broadley

Klenk

Thies

et al. 2018

Sensors

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show abstract

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Casilari

Silva

2022

Measurement

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Coarse-to-Fine Activity Annotation and Recognition Algorithm for Solitary Older Adults

Chu

et al. 2020

IEEE Access

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Older adults want to remain independent with dignity for as long as possible, especially the solitary older adults. Activity recognition plays an essential role in elderly care and rehabilitation by detecting activity shifts among the elderly population. Despite over a decade of research and development in activity recognition, accurate and reliable systems for older adults in use are few. We propose an automatic data collecting and labeling system by addressing the annotation issue, and a novel coarse-to-fine activities of daily living(ADLs) recognition algorithm for older adults, by combining supervised and unsupervised machine learning methods. The automatic data collecting and labeling system targets at the annotation issue caused by the diversity of ADLs in free-living situations. Multiple sensors fusion strategy is employed to interpret and annotate the ADLs. Leveraging supervised and unsupervised machine learning methods, we can discover and recognize ambulatory and trivial ADLS for older adults. The performance of the automatic data collecting and labeling system is double-checked in a four days long test. With the reliable ground truth, we evaluate the coarse-to-fine ADLs recognition algorithm. The performance of our algorithm is promising, the recognition accuracy is larger than 91%.

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Challenges in Studying Falls of Community-Dwelling Older Adults in the Real World

Cited by 6 publications

References 8 publications

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Coarse-to-Fine Activity Annotation and Recognition Algorithm for Solitary Older Adults

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