Does the Evaluation of Gait Quality During Daily Life Provide Insight Into Fall Risk? A Novel Approach Using 3-Day Accelerometer Recordings

Weiss, Aner; Brozgol, Marina; Dorfman, Moran; Herman, Talia; Shema, Shirley; Giladi, Nir; Hausdorff, Jeffrey M.

doi:10.1177/1545968313491004

Cited by 272 publications

(293 citation statements)

References 50 publications

Supporting

Mentioning

243

Contrasting

Unclassified

Order By: Relevance

“…Previous work has shown that quality of gait metrics from three days of normal activity can identify elderly people at risk of falling as good as clinically based tests [9]. Being able to assess falls risk via wearable sensor use as patients go about their daily life would allow many more patients to be monitored, as opposed to patients only having their falls risk assessed upon a visit to a clinician.…”

Section: Discussionmentioning

confidence: 99%

“…Being able to assess falls risk via wearable sensor use as patients go about their daily life would allow many more patients to be monitored, as opposed to patients only having their falls risk assessed upon a visit to a clinician. While this previous work [9] shows that falls risk can be obtained from gait data from the home and community, there is a possibility that the additional knowledge of the external environment during certain gait events may allow for a more accurate falls risk prediction as well as giving clinicians powerful information to guide rehabilitation programs to reduce the chance of falling.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Does external walking environment affect gait patterns?

Patterson

Whelan

Reginatto

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Abstract-The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting. I. INTRODUCTIONFalling is a common occurrence in older adults and is a leading cause of serious injury, loss of independence, and nursing-home admission [1]. Prevention of falls results in prevention of injury and maintenance of independent living [2]. It is important to identify elderly persons who are particularly at risk of suffering a fall. Identification of high risk individuals allows fall prevention interventions to be directed appropriately.Research has shown that metrics from supervised mobility assessments can be used to identify elderly patients who may be at an increased risk of falling. It has been shown that stride time variability from a supervised walking trial can be used to predict risk of falling [3]. More recently it has been shown that wearable sensor metrics from a timed up and go (TUG) test can be used to predict risk of falling [4]. Such tests are very useful because they can identify individuals who are at an increased risk of falling. Such individuals should be directed towards interventions to reduce their risk of falling. A. Inomata is with Fujitsu Laboratories Japan.A limitation of the methods presented above [3,4] is that these tests require clinical supervision to be performed. With advancing technology is it now possible to use wearable sensors to monitor mobility as people go about their daily lives, without the need for a visit to the hospital or clinic [5]. Utilization of mobility data from daily life to identify falls risk would mean that a much larger group of elderly individuals could have their falls risk monitored compared to using clinically supervised testing alone. An added benefit would be that real world mobility data would be used to assess falls risk, as opposed to mobility data from the controlled supervised clinical environment.Lesson's learned regarding mobility metrics and their relationship to falls risk from the supervised environment cannot be directly applied to an unsupervised environment. It is not ...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Does external walking environment affect gait patterns?

Patterson

Whelan

Reginatto

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…<Table 1 -see end of document> Measurement of gait during free-living is difficult when using inertial sensor-based wearables only due to the lack of contextual information. Thus, initial work to quantify free-living gait started at low macro resolutions of 60-seconds [42], progressing to 10-seconds [43] ensuring, with a degree of certainty, that steady state gait was measured due to the cyclical nature of inertial gait signals.…”

Section: Activity Recognition: Macro and Micro Gaitmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

View full text Add to dashboard Cite

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) are one approach to offer a viable means for ubiquitous, sustainable and scalable monitoring in habitual free-living environments. Gait has been presented as a relevant (bio) marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of recent wearable gait assessment literature, focusing on the need for an algorithm fusion approach to measurement cumulating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Opportunities for how this domain needs to progress are also summarised. Highlights• Wearables can meet older adults' needs for independent living.• Gait assessment is a (bio)marker within ageing and different pathologies.• Measuring gait with wearables has been innovative but fraught with inconsistencies.

show abstract

“…While such measures can potentially be useful in aiding general weight loss and providing motivation for users to become more active, there is also a need to determine more detailed quality of mobility metrics [1]. Research in the mobility space has linked such detailed mobility metrics to important health related outcomes such as falls risk [2], Parkinson's disease [1,3], Multiple sclerosis score [4] and cerebral palsy gait assessment [5].…”

Section: Introductionmentioning

confidence: 99%