Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders

Hagler, Stuart; Austin, Daniel; Hayes, Tamara L.; Kaye, Jeffrey; Pavel, M.

doi:10.1109/tbme.2009.2036732

Cited by 156 publications

(117 citation statements)

References 28 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…One common approach to continuously monitor gait consists in instrumenting the environment with vision based systems and/or with passive infrared motion sensors [11]- [13]. These systems achieve high precision in capturing important gait parameters, such as stride duration and walking speed.…”

Section: Introductionmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

View full text Add to dashboard Cite

Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real-time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ∼ 84% accuracy in the recognition of abnormal gait segments lasting ∼ 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

show abstract

Section: Introductionmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The assessment of walking speed and its data validation process are described in detail elsewhere. 6,23 In brief, motion sensors with a restricted field of view were installed along a hallway so they would detect only when a person passed directly under them. Data from all sensors were wirelessly received by a dedicated research computer placed in the subject's home and then were time-stamped and stored in a structured query language database.…”

mentioning

confidence: 99%

In-home walking speeds and variability trajectories associated with mild cognitive impairment

Dodge¹,

Mattek²,

Austin³

et al. 2012

Neurology

Self Cite

197

163

View full text Add to dashboard Cite

Objective: To determine whether unobtrusive long-term in-home assessment of walking speed and its variability can distinguish those with mild cognitive impairment (MCI) from those with intact cognition. Methods:Walking speed was assessed using passive infrared sensors fixed in series on the ceiling of the homes of elderly individuals participating in the Intelligent Systems for Assessing Aging Change (ISAAC) cohort study. Latent trajectory models were used to analyze weekly mean speed and walking speed variability (coefficient of variation [COV]).Results: ISAAC participants living alone included 54 participants with intact cognition, 31 participants with nonamnestic MCI (naMCI), and 8 participants with amnestic MCI at baseline, with a mean follow-up of 2.6 Ϯ 1.0 years. Trajectory models identified 3 distinct trajectories (fast, moderate, and slow) of mean weekly walking speed. Participants with naMCI were more likely to be in the slow speed group than in the fast (p ϭ 0.01) or moderate (p ϭ 0.04) speed groups. For COV, 4 distinct trajectories were identified: group 1, the highest baseline and increasing COV followed by a sharply declining COV; groups 2 and 3, relatively stable COV; and group 4, the lowest baseline and decreasing COV. Participants with naMCI were more likely to be members of either highest or lowest baseline COV groups (groups 1 or 4), possibly representing the trajectory of walking speed variability for early-and late-stage MCI, respectively. It is of substantial importance to detect dementia at its earliest phases to sustain independence, to optimize treatment, to understand preclinical biology, and to ultimately develop prevention strategies. Past studies have found that slower walking speed and poorer motor function are associated with mild cognitive impairment (MCI) and are predictors of progression to frank dementia. [1][2][3][4] However, it is difficult to identify changes in these functions because changes evolve slowly over time and change measures have high test-to-test variability. Further, current methods for assessing this change rely largely on sparsely spaced or annual assessments that provide too few data points to discern subtle changes. An alternative to this approach is to deploy unobtrusive passive monitoring systems in people's homes, providing continuous assessment of daily activity and behaviors of interest. This kind of pervasive computing model has been established by the Intelligent Systems for Assessing Aging Change (ISAAC) study, 5,6 Conclusion:

show abstract

“…These systems can continually or continuously monitor users' activities or physical condition. Examples of mounted systems include monitoring systems based on cameras [33,15] or infrared rays [22,35] installed at the user's home. With regard to studies on houses that can monitor residents' activity or health, the Georgia Tech Aware Home [31] and the Welfare Techno House [29,47] are good examples.…”

Section: Monitoring Daily Activities and Health: Envi Ronmental And Pmentioning

confidence: 99%

Smartphone-Based Gait Measurement Application for Exercise and Its Effects on the Lifestyle of Senior Citizens

Miura

Yabu

Hiyama

et al. 2015

Human-Computer Interaction – INTERACT 2015

View full text Add to dashboard Cite

Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders

Cited by 156 publications

References 28 publications

An On-Node Processing Approach for Anomaly Detection in Gait

An On-Node Processing Approach for Anomaly Detection in Gait

In-home walking speeds and variability trajectories associated with mild cognitive impairment

Smartphone-Based Gait Measurement Application for Exercise and Its Effects on the Lifestyle of Senior Citizens

Contact Info

Product

Resources

About