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The article focuses on a study conducted to explore further the relationship of human figure drawing ability and vestibular processing function as measured by postrotary nystagmus. The results of this study support the contention of previous investigators that some learning-disabled children can be differentiated based on their performance on measures of vestibular-ocular function. This study also suggests that human figure drawing may be used as one indicator of vestibular related dysfunction in some learning-disabled children and that the postrotary nystagmus-figure drawing relationship operates independently of IQ.

We compared the performance of the Kinect skeletal data with the Kinect depth data in capturing different gait parameters during the Timed-up and Go Test (TUG) and Figure of 8 Walk Test (F8W). The gait parameters considered were stride length, stride time, and walking speed for the TUG, and number of steps and completion time for the F8W. A marker-based Vicon motion capture system was used for the ground-truth measurements. Five healthy participants were recruited for the experiment and were asked to perform three trials of each task. Results show that depth data analysis yields stride length and stride time measures with significantly low percentile errors as compared to the skeletal data analysis. However, the skeletal and depth data performed similar with less than 3% of absolute mean percentile error in determining the walking speed for the TUG and both parameters of F8W. The results show potential capabilities of Kinect depth data analysis in computing many gait parameters, whereas, the Kinect skeletal data can also be used for walking speed in TUG and F8W gait parameters.

In this paper, we present an interactive physical therapy system (IPTS) for remote quantitative assessment of clients in the home. The system consists of two different interactive interfaces connected through a network, for a real-time low latency video conference using audio, video, skeletal, and depth data streams from a Microsoft Kinect. To test the potential of IPTS, experiments were conducted with 5 independent living senior subjects in Kansas City, MO. Also, experiments were conducted in the lab to validate the real-time biomechanical measures calculated using the skeletal data from the Microsoft Xbox 360 Kinect and Microsoft Xbox One Kinect, with ground truth data from a Vicon motion capture system. Good agreements were found in the validation tests. The results show potential capabilities of the IPTS system to provide remote physical therapy to clients, especially older adults, who may find it difficult to visit the clinic.

In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0.99 and repeatability coefficients between 0.7% and 6.6% was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.

Strategically weighted vests are currently being used to treat patients with Parkinson's, Multiple Sclerosis, and ataxia. While studies have been conducted to demonstrate the effectiveness of these vests, there has been very little research into the mechanisms that give rise to the vest's results. This study demonstrates the ability to capture gait parameters from depth images[1] in the home with sufficient sensitivity to support future investigation of the weighted vest intervention. The study also explores multiple metrics, using in-home gait sensing, to study a subject's ambulatory ability including gait mechanics, uncertainty in motion, and gait cadence. We then investigate the effects of these vests on a subject's ambulation by examining these metrics both before and after the vest is worn. While only four subjects were used, results are promising, showing a statistically significant and clinically significant change in many of these metrics as a result of the vest. The cases presented here concern two subjects, one with a "tight" gait caused by Progressive Supranuclear Palsy, and the second with an excessively "loose" gait due to Parkinson's disease. We show that in both subjects, using the vest immediately moved the metrics in a direction beneficial to the subject's clinical condition. This result concurs with clinical observations as measured using various clinical fall risk instruments.

Falls are a significant cause of injury and accidental death among persons over the age of 65. Gait velocity is one of the parameters which have been correlated to the risk of falling. We aim to build a system which monitors gait in seniors and reports any changes to caregivers, who can then perform a clinical assessment and perform corrective and preventative actions to reduce the likelihood of falls. In this paper, we deploy a Doppler radar-based gait measurement system into the apartments of thirteen seniors. In scripted walks, we show the system measures gait velocity with a mean error of 14.5% compared to the time recorded by a clinician. With a calibration factor, the mean error is reduced to 10.5%. The radar is a promising sensing technology for gait velocity in a day-to-day senior living environment.

In this paper, we present a method for quantitatively and objectively assessing 180 degree turns using low cost video sensors. A three-dimensional voxel reconstruction, which is built using silhouettes captured from two calibrated web camera views, is used to represent the human body. Experiments were conducted in which participants performed the standard Timed Up and Go tests where 180 degree turns are evaluated. Our two calibrated cameras captured the images during the test. Two key parameters including turn time and turn steps are extracted using the voxel data. Good agreement for the turn time was found for our system compared to the expert rating. The extracted numbers of turn steps are one step less than the expert rating in many test runs. The difference comes mainly from the nature of the pivot turns, and the turn time difference between the expert rating and the algorithm, namely the determination of the time duration from the beginning to the end of the turn. The development of this technology provides potential for assessing 180 degree turns in the home setting as part of a balance, stability and fall risk assessment tool.

In this paper, we present a method for extracting body sway parameters from a three-dimensional voxel reconstruction, which is built using silhouettes captured from two calibrated web camera views. The results were validated with a Vicon motion capture system. Experiments were conducted in which subjects stand and sway in the anterior-posterior direction and then in the lateral directions with two different frequencies. In addition, experiments were also conducted where subjects walked in a straight path at different speeds. Through the experiment, the Vicon cameras recorded the motion of reflective markers attached to subjects, and our two calibrated cameras captured the images. Good agreement was found with our system compared to the Vicon results, given the limitation of voxel space resolution and frame rate. The development of this technology provides potential capability of measuring body sway in daily living environment for elderly people, and can be used as part of a balance, stability and fall risk assessment tool.

In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercises - treadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.

Today, eldercare demands a greater degree of versatility in healthcare. Automatic monitoring devices and sensors are under development to help senior citizens achieve greater autonomy, and, as situations arise, alert healthcare providers. In this paper, we study gait patterns based on extracted silhouettes from image sequences. Three features are investigated through two different image capture perspectives: shoulder level, spinal incline, and silhouette centroid. Through the evaluation of fourteen image sequences representing a range of healthy to frail gait styles, features are extracted and compared to validation results using a Vicon motion capture system. The results obtained show promise for future studies that can increase both the accuracy of feature extraction and pragmatism of machine monitoring for at-risk elders.