Room-scale 3D position tracking enables users to explore a virtual environment by physically walking, which improves comfort and the level of immersion. However, when users walk with their eyesight blocked by a head-mounted display, they may unexpectedly lose their balance and fall if they bump into real-world obstacles or unintentionally shift their center of mass outside the margin of stability. This paper evaluates balance recovery methods and intervention timing during the use of VR with the assumption that the onset of a fall is given. Our experiment followed the tether-release protocol during clinical research and induced a fall while a subject was engaged in a secondary 3D object selection task. The experiment employed a two-by-two design that evaluated two assistive techniques, i.e., video-see-through and auditory warning at two different timings, i.e., at fall onset and 500ms prior to fall onset. The data from 17 subjects showed that video-see-through triggered 500 ms before the onset of fall can effectively help users recover from falls. Surprisingly, video-see-through at fall onset has a significant negative impact on balance recovery and produces similar results to those of the baseline condition (no intervention).

Falls in older people are a major challenge to public health. A wearable fall detector can detect falls automatically based on kinematic information of the human body, allowing help to arrive sooner. To date, most studies have focused on the accuracy of an offline algorithm to distinguish real-world or simulated falls from activities of daily living, while neglecting the false alarm rate and battery life of a real device. To address these two important metrics, which significantly influence user compliance, this paper proposes a low-power fall detector using triaxial accelerometry and barometric pressure sensing. This fall detector minimizes power consumption using both hardware- and firmware-based techniques. Additionally, the fall detection algorithm used in this device is optimized to achieve a balance between sensitivity and false alarm rate, while minimizing the power consumption due to algorithm execution. The fall detector achieved a high sensitivity (91%) with a low false alarm rate (0.1149 alarms per hour), and a commercially-viable battery life (1125 days).

We used body-worn inertial sensors to quantify differences in semi-free-living gait between stairs and on normal flat ground in older adults, and investigated the utility of assessing gait on these terrains for predicting the occurrence of multiple falls. Eighty-two community-dwelling older adults wore two inertial sensors, on the lower back and the right ankle, during several bouts of walking on flat surfaces and up and down stairs, in between rests and activities of daily living. Derived from the vertical acceleration at the lower back, step rate was calculated from the signal's fundamental frequency. Step rate variability was the width of this fundamental frequency peak from the signal's power spectral density. Movement vigor was calculated at both body locations from the signal variance. Partial Spearman correlations between gait parameters and physiological fall risk factors (components from the Physiological Profile Assessment) were calculated while controlling for age and gender. Overall, anteroposterior vigor at the lower back in stair descent was lower in subjects with longer reaction times. Older adults walked more slowly on stairs, but they were not significantly slower on flat surfaces. Using logistic regression, faster step rate in stair descent was associated with multiple prospective falls over 12 months. No significant associations were shown from gait parameters derived during walking upstairs or on flat surfaces. These results suggest that stair descent gait may provide more insight into fall risk than regular walking and stair ascent, and that further sensor-based investigation into unsupervised gait on different terrains would be valuable.

Falls remain an important problem in older people. There is strong evidence that falls can be prevented with appropriately designed intervention programs. To start a targeted fall prevention program, a first step is to identify those at high risk of falls. Sensor-based tests hold great promise for more frequent and accurate assessment of fall risk in clinical and home settings. The aims of this study were to (a) empirically examine the feasibility of the iStoppFalls (Information and communications technology–based System to Predict & Prevent Falls) assessment, a Kinect and inertial sensor-based test for regular and unsupervised fall risk assessments at home, (b) investigate the experience of older adults with this home-based self-assessment, and (c) make recommendations for future assessments. The iStoppFalls assessment system was installed into the homes of 62 community-living older people in Australia, Germany, and Spain for the duration of 4 months. Participants were asked to perform at least 1 assessment each month. The system use and the user experience were evaluated. To our knowledge, these are the first results on the long-term use of an unsupervised directed routine fall risk assessment system at private homes. In total, 241 assessments were independently performed by the participants. Most participants felt positive about their experience and could see themselves continuing with the assessment on a regular basis. Through the analysis the user motivation, the design and selection of appropriate tests, the user feedback, the reliability and usability of the applied technology, the frequency and duration of the assessment and the safety and support aspects were identified as important characteristics of a home-based self-assessment. The findings demonstrate the feasibility of a sensor-based self-assessment for fall risk but also highlight that further work is necessary. Future research should consider the necessary design requirements identified by this study.

Falls are the number one cause of injuries in the elderly. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or a medical rescue group for immediate assistance, mitigating fall-related injuries. However, most studies on fall detection to date have focused on the accuracy of detection while neglecting power efficiency and battery life, and hence the developed fall detectors usually cannot operate for a long period (a year or more) without recharging or replacing their batteries. This paper presents a low-power fall detector that utilizes triaxial accelerometry and barometric pressure sensing. This fall detector reduces its power consumption through both hardware- and firmware-based approaches. This study also incorporates several human trials to develop and evaluate the device, including simulated falls and activities of daily living. A benchtop power measurement test is also conducted to estimate the battery life with data from a one-week free-living trial. These experiments show that the fall detector achieves high sensitivity (97.5% and 93.0%) and specificity (93.2% and 87.3%) on training and testing datasets, while providing an estimated battery life of 664.9 days.