Assessing fall risk using wearable sensors: a practical discussion

Shany, Tal; Redmond, Stephen J.; Marschollek, Michael; Lovell, Nigel H.

doi:10.1007/s00391-012-0407-2

Cited by 57 publications

(37 citation statements)

References 29 publications

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“…A review of sensor technologies targeting fall prevention in hospitals and other institutional settings (most frequently bed or chair alarms) concluded additional research is needed to “develop sensor systems which cover rooms and units 24 hours a day” and also cover larger areas"not only around the bed or a chair.” [23, page 751]. In a review of wearable sensors for assessment of fall risk, authors raise concerns that unsupervised assessment of fall risk in the community should not be the end goal because appropriate interventions should be planned to reduce fall risk [24]. However, since most falls of older adults go unreported, detecting an actual fall may represent the first opportunity clinicians have to offer fall risk reduction interventions [25].…”

Section: Why Early Detection Of Health Change?mentioning

confidence: 99%

A New Paradigm of Technology-Enabled ‘Vital Signs' for Early Detection of Health Change for Older Adults

Rantz

Skubic²,

Popescu³

et al. 2014

Gerontology

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Environmentally embedded (nonwearable) sensor technology is in continuous use in elder housing to monitor a new set of ‘vital signs' that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to healthcare staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or healthcare providers. Discovering these new functional status ‘vital signs', developing automated methods for interpreting them, and alerting others when changes occur have the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.

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Section: Why Early Detection Of Health Change?mentioning

confidence: 99%

A New Paradigm of Technology-Enabled ‘Vital Signs' for Early Detection of Health Change for Older Adults

Rantz

Skubic²,

Popescu³

et al. 2014

Gerontology

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“…One of the accelerometer-based physical-activity-monitoring review papers [26] provided only a brief review of inertial-sensor-based fall risk assessment. Shany et al [27] provided an interesting discussion of wearable sensors for fall risk assessment, focusing on high-level methodologies when assessing structured or unstructured movements in supervised or unsupervised environments.…”

Section: Introductionmentioning

confidence: 99%

Review of fall risk assessment in geriatric populations using inertial sensors

Howcroft

Kofman

Lemaire

2013

J NeuroEngineering Rehabil

302

209

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BackgroundFalls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.MethodsForty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.ResultsInertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).ConclusionsInertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

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“…Since many falls occur in the home and community, where hazards are commonplace, it has been suggested to assess fall risk in these complex “natural” environments [16,17]. Further, there are significant concerns that people working in busy clinical settings do not have the time or equipment required to perform thorough objective fall risk assessments [18], and even where possible, these clinical settings do not emulate the natural home and community environment [16,17]. Additionally, fall risk assessment based on performance-based tests may be insensitive in those with cognitive impairment [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, fall risk assessment based on performance-based tests may be insensitive in those with cognitive impairment [19,20]. There is a need for simple clinical tools that can objectively assess fall risk in the rapidly growing population of cognitively impaired [18,21]. However, to date, there has been limited work performed in this important area [18,21,22].…”

Section: Introductionmentioning

confidence: 99%

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Sensor-Derived Physical Activity Parameters Can Predict Future Falls in People with Dementia

Schwenk¹,

Hauer

Zieschang

et al. 2014

Gerontology

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Background: There is a need for simple clinical tools that can objectively assess the fall risk in people with dementia. Wearable sensors seem to have the potential for fall prediction; however, there has been limited work performed in this important area. Objective: To explore the validity of sensor-derived physical activity (PA) parameters for predicting future falls in people with dementia. To compare sensor-based fall risk assessment with conventional fall risk measures. Methods: This was a cohort study of people with confirmed dementia discharged from a geriatric rehabilitation ward. PA was quantified using 24-hour motion-sensor monitoring at the beginning of the study. PA parameters (percentage of walking, standing, sitting, and lying; duration of single walking, standing, and sitting bouts) were extracted using specific algorithms. Conventional assessment included performance-based tests (Timed Up and Go Test, Performance-Oriented Mobility Assessment, 5-chair stand) and questionnaires (cognition, ADL status, fear of falling, depression, previous faller). Outcome measures were fallers (at least one fall in the 3-month follow-up period) versus non-fallers. Results: 77 people were included in the study (age 81.8 ± 6.3; community-dwelling 88%, institutionalized 12%). Surprisingly, fallers and non-fallers did not differ on any conventional assessment (p = 0.069-0.991), except for ‘previous faller' (p = 0.006). Interestingly, several PA parameters discriminated between the groups. The ‘walking bout average duration', ‘longest walking bout duration' and ‘walking bout duration variability' were lower in fallers, compared to non-fallers (p = 0.008-0.027). The ‘standing bout average duration' was higher in fallers (p = 0.050). Two variables, ‘walking bout average duration' [odds ratio (OR) 0.79, p = 0.012] and ‘previous faller' (OR 4.44, p = 0.007) were identified as independent predictors for falls. The OR for a ‘walking bout average duration' <15 s for predicting fallers was 6.30 (p = 0.020). Combining ‘walking bout average duration' and ‘previous faller' improved fall prediction (OR 7.71, p < 0.001, sensitivity/specificity 72%/76%). Discussion: Results demonstrate that sensor-derived PA parameters are independent predictors of the fall risk and may have higher diagnostic accuracy in persons with dementia compared to conventional fall risk measures. Our findings highlight the potential of telemonitoring technology for estimating the fall risk. Results should be confirmed in a larger study and by measuring PA over a longer period of time.

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Assessing fall risk using wearable sensors: a practical discussion

Cited by 57 publications

References 29 publications

A New Paradigm of Technology-Enabled ‘Vital Signs' for Early Detection of Health Change for Older Adults

A New Paradigm of Technology-Enabled ‘Vital Signs' for Early Detection of Health Change for Older Adults

Review of fall risk assessment in geriatric populations using inertial sensors

Sensor-Derived Physical Activity Parameters Can Predict Future Falls in People with Dementia

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