Gina Sprint

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measures. The timed up and go (TUG) test is one such assessment recently instrumented with technology in several studies, yielding promising results toward the future of automating clinical assessments. Potential benefits of technological TUG implementations include additional performance parameters, generated reports, and the ability to be self-administered in the home. In this paper, we provide an overview of the TUG test and technologies utilized for TUG instrumentation. We then critically review the technological advancements and follow up with an evaluation of the benefits and limitations of each approach. Finally, we analyze the gaps in the implementations and discuss challenges for future research toward automated self-administered assessment in the home.

Using Smart Homes to Detect and Analyze Health Events

Smart homes offer an unprecedented opportunity to unobtrusively monitor human behavior in everyday environments and to determine whether relationships exist between behavior and health changes. Behavior change detection (BCD) can be used to identify changes that accompany health events, which can potentially save lives.

Detecting Health and Behavior Change by Analyzing Smart Home Sensor Data

Smart home environments offer an unprecedented opportunity to unobtrusively monitor human behavior. Sensor data collected from smart homes can be labeled using activity recognition to help determine whether relationships exist between behavior in the home and health changes. To detect and analyze behavior changes that accompany health events, we introduce the behavior change detection (BCD) approach. BCD detects activity timing and duration changes between windows of time, determines the significance of the detected changes, and analyzes the nature of the changes. We demonstrate our approach using two case studies for older adults living in smart homes who experienced major health events, including cancer treatment and insomnia. Our algorithm detects behavior changes consistent with the medical literature for these cases. The results suggest the changes can be automatically detected using BCD. The proposed smart home, activity recognition algorithms, and change detection approach are useful data mining techniques for understanding the behavioral effects of major health conditions.

Predicting Functional Independence Measure Scores During Rehabilitation With Wearable Inertial Sensors

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon the standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a seven-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported.

Wearable sensors in ecological rehabilitation environments

Rehabilitation after injury or stroke is a long process towards regaining function, mobility, and independence. Changes exhibited in these areas tend to be subtle and highly dependent on the patient, their injury, and the intensity of rehabilitation efforts. To provide a fine-grained assessment of patient progress, we undertook a study to quantitatively capture movements during inpatient rehabilitation. We utilized wearable inertial sensors to collect data from participants receiving therapy services at an inpatient rehabilitation facility. Participant performance was recorded in an ecological environment on a sequence of ambulatory tasks. A custom software system was developed to process sensor signals and compute metrics describing ambulation. A comparison of metrics one week apart suggests quantifiable changes in movement.

Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation

Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers.

Designing Wearable Sensor-Based Analytics for Quantitative Mobility Assessment

Wearable sensors are gaining traction in various healthcare domains, including patient mobility assessment performed in rehabilitation environments. Typically, clinical observations by therapists are used to characterize patient movement abilities and progress. More precise quantitative measurements of patient performance can be collected with wearable inertial sensors. Highly useful quantitative information and visual presentations of wearable sensor data are critical in gaining therapist acceptance of the technology and improving the therapy experience for patients. To bridge the gap between design of mobility monitoring technology and actual use of the technology, we report responses from interviews conducted with physical therapy providers at an inpatient rehabilitation facility. The information presented during the interviews includes results from our wearable sensor-based mobility assessment algorithms. Our smart computing algorithms utilize wearable sensor data to extract patient movement metrics, train clinical assessment prediction models, and visualize the data. The interview results indicate therapy providers are interested in using wearable sensors and wearable sensor- based metrics, prediction tools, and visualizations while they provide therapy services for their patients. Based on therapist feedback, we suggest future research directions that may increase the clinical utility and adoption of wearable sensor systems and data visualization for mobility assessment.

Quantitative assessment of lower limb and cane movement with wearable inertial sensors

Individuals with an age, injury, or disease-related mobility impairment often utilize a walking aid, such as a cane, to increase safety and stability during ambulation. Many individuals use a cane incorrectly and demonstrate altered gait patterns. Consequently, measuring the relationship between cane use and gait characteristics has potential to provide users, clinicians, and caregivers insightful information about cane-assisted walking. In this paper, we investigate fine-grained, objective measures of cane movement acquired from wearable inertial sensors. Specifically, we compute quantifications of swing and stance variability for both lower limbs and a cane device. We also introduce a novel visualization, the stance and swing phase plot, to facilitate insights into the sensor data. The computed gait parameters and visualization can potentially inform users and clinicians about assistive device usage over time and provide feedback about correct movement. We demonstrate the utility of the proposed algorithms with inertial sensor data collected from two patients undergoing inpatient stroke rehabilitation.

Using wrist-worn sensors to measure and compare physical activity changes for patients undergoing rehabilitation

Wrist-worn sensors have increased in popularity in health care settings. As the use of wrist-worn sensors increases, a better understanding is needed of how to detect changes in behavior as well as an ability to quantify such changes. We introduce a statistical method to address this need. In this study, we used Fitbit Charge Heart Rate devices with two separate populations to continuously record data. There were eight participants in the healthy control group and nine in the hospitalized inpatient rehabilitation group. We performed comparisons both within the groups and between groups on the gathered step count and heart rate data. The inpatient rehabilitation group showed improved step count changes between the first half of the study participation and the second half. Heart rate did not show significant changes for either the healthy control group or inpatient rehabilitation group across time. We conclude that our statistical change analysis applied to wrist-worn sensors can effectively detect changes in physical activity that provides valuable information to patients as well as their healthcare care providers.

Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors

Restoration of functional independence in gait and vehicle transfer ability is a common goal of inpatient rehabilitation. Currently, ambulation changes tend to be subjectively assessed by clinicians. To investigate more precise objective assessment of progress in inpatient rehabilitation, we quantitatively assessed gait and transfer performances over the course of rehabilitation with wearable inertial sensors for 20 patients receiving inpatient rehabilitation services. Participant performance was recorded on a sequence of ambulatory tasks that closely resemble everyday activities. We developed a custom software system to process sensor signals and compute metrics that characterize ambulation performance. We quantified changes in gait and transfer ability by performing a repeated measures comparison of the metrics one week apart. Metrics showing the greatest improvement are walking speed, stride regularity, acceleration root mean square, walking smoothness, shank peak angular velocity, and shank range of motion. Wearable sensor-derived metrics can potentially provide rehabilitation therapists with additional valuable information to aid in treatment decisions.