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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2012)

Assurance of outcome is critical for patients that are afflicted with neurological disorders (for example stroke that introduces hemiparetic gait) and motion is a critical indicator of outcome. However outcome in the community environment is very difficult to determine. Wearable sensing system, due to its low cost, energy efficient, non-intrusive and applicable for large-scale development, is feasible for remote, continuous monitoring of motion and activities and thus provide outcome measures in the community. Towards this goal, this thesis explores the following problems:

1. A precise motion classification system is introduced.

It is applicable for diverse subjects whose motion ability ranges from very disabled to very capable and suitable for complicated community environment. Critical kinematic parameters of motion is modeled, characterized and validated, which provides direct outcome measures in the community. Those methods are applied to a large-scale trial with stroke patients. The problems encountered in large-scale trial and the corresponding engineering solutions are summarized.

2. For effective feedback provision, a PCA based visualization method is proposed, where gait quality evolution path is shown.

It also introduces a gait quality vector and in particular, a metric to measure uncertainty in gait.

3. The individualized motion classification model is extended into a population based analysis. First an indexing method for data dimension reduction is proposed. The indexing method expedites the processing speed and maintains least critical information loss for motion analysis. I also hypothesize and validate a power law exist between the diversity of motion (for example gait pattern) and the largest approximate cluster. Second, hierarchical clustering method is employed to derive the primitive gait patterns from the stroke population. The developed techniques help scale the motion classification system for big data.

    Cover page: Large Scale Human Activity Monitoring for Diverse Subjects
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2011)

    Background and purpose

    Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials.

    Methods

    Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs.

    Results

    A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios.

    Conclusions

    Test-retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.
      Cover page: Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke
      • Article
      • Peer Reviewed
      LBL Publications (2019)
      The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.
        Cover page: Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2015)

        Background

        Walking-related disability is the most frequent reason for inpatient stroke rehabilitation. Task-related practice is a critical component for improving patient outcomes.

        Objective

        To test the feasibility of providing quantitative feedback about daily walking performance and motivating greater skills practice via remote sensing.

        Methods

        In this phase III randomized, single blind clinical trial, patients participated in conventional therapies while wearing wireless sensors (triaxial accelerometers) at both ankles. Activity-recognition algorithms calculated the speed, distance, and duration of walking bouts. Three times a week, therapists provided either feedback about performance on a 10-meter walk (speed only) or walking speed feedback plus a review of walking activity recorded by the sensors (augmented). Primary outcomes at discharge included total daily walking time, derived from the sensors, and a timed 15-meter walk.

        Results

        Sixteen rehabilitation centers in 11 countries enrolled 135 participants over 15 months. Sensors recorded more than 1800 days of therapy, 37 000 individual walking bouts, and 2.5 million steps. No significant differences were found between the 2 feedback groups in daily walking time (15.1 ± 13.1 vs 16.6 ± 14.3 minutes, P = .54) or 15-meter walking speed (0.93 ± 0.47 vs 0.91 ± 0.53 m/s, P = .96). Remarkably, 30% of participants decreased their total daily walking time over their rehabilitation stay.

        Conclusions

        In this first trial of remote monitoring of inpatient stroke rehabilitation, augmented feedback beyond speed alone did not increase the time spent practicing or improve walking outcomes. Remarkably modest time was spent walking. Wireless sensing, however, allowed clinicians to audit skills practice and provided ground truth regarding changes in clinically important, mobility-related activities.
          Cover page: SIRRACT
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