Fall detection in homes of older adults using the Microsoft Kinect.

A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.

Automated health alerts from Kinect-based in-home gait measurements.

A method for automatically generating alerts to clinicians in response to changes in in-home gait parameters is investigated. Kinect-based gait measurement systems were installed in apartments in a senior living facility. The systems continuously monitored the walking speed, stride time, and stride length of apartment residents. A framework for modeling uncertainty in the residents' gait parameter estimates, which is critical for robust change detection, is developed; along with an algorithm for detecting changes that may be clinically relevant. Three retrospective case studies, of individuals who had their gait monitored for periods ranging from 12 to 29 months, are presented to illustrate use of the alert method. Evidence suggests that clinicians could be alerted to health changes at an early stage, while they are still small and interventions may be most successful. Additional potential uses are also discussed.

Evaluation of the Microsoft Kinect for screening ACL injury.

A study was conducted to evaluate the use of the skeletal model generated by the Microsoft Kinect SDK in capturing four biomechanical measures during the Drop Vertical Jump test. These measures, which include: knee valgus motion from initial contact to peak flexion, frontal plane knee angle at initial contact, frontal plane knee angle at peak flexion, and knee-to-ankle separation ratio at peak flexion, have proven to be useful in screening for future knee anterior cruciate ligament (ACL) injuries among female athletes. A marker-based Vicon motion capture system was used for ground truth. Results indicate that the Kinect skeletal model likely has acceptable accuracy for use as part of a screening tool to identify elevated risk for ACL injury.

Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect.

A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.

In-home fall risk assessment and detection sensor system.

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported.

Capturing habitual, in-home gait parameter trends using an inexpensive depth camera.

Results are presented for measuring the gait parameters of walking speed, stride time, and stride length of five older adults continuously, in their homes, over a four month period. The gait parameters were measured passively, using an inexpensive, environmentally mounted depth camera, the Microsoft Kinect. Research has indicated the importance of measuring a person's gait for a variety of purposes from fall risk assessment to early detection of health problems such as cognitive impairment. However, such assessments are often done infrequently and most current technologies are not suitable for continuous, long term use. For this work, a single Microsoft Kinect sensor was deployed in four apartments, containing a total of five residents. A methodology for generating trends in walking speed, stride time, and stride length based on data from identified walking sequences in the home is presented, along with trend estimates for the five participants who were monitored for this work.

Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing.

We present an analysis of measuring stride-to-stride gait variability passively, in a home setting using two vision based monitoring techniques: anonymized video data from a system of two web-cameras, and depth imagery from a single Microsoft Kinect. Millions of older adults fall every year. The ability to assess the fall risk of elderly individuals is essential to allowing them to continue living safely in independent settings as they age. Studies have shown that measures of stride-to-stride gait variability are predictive of falls in older adults. For this analysis, a set of participants were asked to perform a number of short walks while being monitored by the two vision based systems, along with a marker based Vicon motion capture system for ground truth. Measures of stride-to-stride gait variability were computed using each of the systems and compared against those obtained from the Vicon.

Extracting footfalls from voxel data.

In this paper, we present a method for extracting footfall locations from three dimensional voxel data created from a pair of silhouettes. With the growth of the elderly population, there is a need for passive monitoring of physical activity to allow older adults to continue living in independent settings. Prior research using anonymized video data has shown good results in passively acquiring information useful for assessing physical function; and, additionally, research has shown that video data anonymized through the use of silhouettes alleviates privacy concerns of older adults towards the technology. Previous work in acquiring gait information from voxel data has not included a technique for identifying individual footfall locations, from which additional information useful for assessing asymmetric gait patterns and other physical parameters may be obtained. Furthermore, visualization of the footfall locations during a walking sequence may provide additional insight to care providers for assessing physical function. To evaluate our approach, participants were asked to walk across a GAITRite electronic mat, used to validate our results, while also being monitored by our camera system. Results show good agreement between the footfalls extracted by our system and those from the GAITRite.