Association between Self-reported Sleep Quality and Single-task Gait in Young Adults: A Study Using Machine Learning.

Objective The objective of the present study was to find biomechanical correlates of single-task gait and self-reported sleep quality in a healthy, young population by replicating a recently published study. Materials and Methods Young adults ( n = 123) were recruited and were asked to complete the Pittsburgh Sleep Quality Inventory to assess sleep quality. Gait variables ( n = 53) were recorded using a wearable inertial measurement sensor system on an indoor track. The data were split into training and test sets and then different machine learning models were applied. A post-hoc analysis of covariance (ANCOVA) was used to find statistically significant differences in gait variables between good and poor sleepers. Results AdaBoost models reported the highest correlation coefficient (0.77), with Support-Vector classifiers reporting the highest accuracy (62%). The most important features associated with poor sleep quality related to pelvic tilt and gait initiation. This indicates that overall poor sleepers have decreased pelvic tilt angle changes, specifically when initiating gait coming out of turns (first step pelvic tilt angle) and demonstrate difficulty maintaining gait speed. Discussion The results of the present study indicate that when using traditional gait variables, single-task gait has poor accuracy prediction for subjective sleep quality in young adults. Although the associations in the study are not as strong as those previously reported, they do provide insight into how gait varies in individuals who report poor sleep hygiene. Future studies should use larger samples to determine whether single task-gait may help predict objective measures of sleep quality especially in a repeated measures or longitudinal or intervention framework.

Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning.

Failure to obtain the recommended 7−9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night's sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7−9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night's sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night's sleep using single-task gait.

Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.

Synthesizing Personalized Construction Safety Training Scenarios for VR Training.

Construction industry has the largest number of preventable fatal injuries, providing effective safety training practices can play a significant role in reducing the number of fatalities. Building on recent advancements in virtual reality-based training, we devised a novel approach to synthesize construction safety training scenarios to train users on how to proficiently inspect the potential hazards on construction sites in virtual reality. Given the training specifications such as individual training preferences and target training time, we synthesize personalized VR training scenarios through an optimization approach. We validated our approach by conducting user studies where users went through our personalized guidance VR training, free exploration VR training, or slides training. Results suggest that personalized guidance VR training approach can more effectively improve users' construction hazard inspection skills.

Mood-Driven Colorization of Virtual Indoor Scenes.

One of the challenging tasks in virtual scene design for Virtual Reality (VR) is causing it to invoke a particular mood in viewers. The subjective nature of moods brings uncertainty to the purpose. We propose a novel approach to automatic adjustment of the colors of textures for objects in a virtual indoor scene, enabling it to match a target mood. A dataset of 25,000 images, including building/home interiors, was used to train a classifier with the features extracted via deep learning. It contributes to an optimization process that colorizes virtual scenes automatically according to the target mood. Our approach was tested on four different indoor scenes, and we conducted a user study demonstrating its efficacy through statistical analysis with the focus on the impact of the scenes experienced with a VR headset.

Effects of Exergaming on Cognition and Gait in Older Adults at Risk for Falling.

PURPOSE: To test whether an 8-wk exergaming (EG) program would improve cognition and gait characteristics compared with a traditional physical exercise (TPE) program in older adults at risk for falling. METHODS: A pilot quasi-experimental study was conducted in adults age ≥65 yr at risk for falls, living in senior communities. Participants enrolled (n = 35) in either exercise program offered twice weekly for 8 wk. Cognition and single-task and dual-task gait characteristics were measured before and after the 8-wk exercise intervention. For each outcome, a repeated-measures ANCOVA adjusted for age, gender, and exercise intensity (ratings of perceived exertion, RPE) was used to examine the group-time interaction. RESULTS: Twenty-nine participants (age, 77 ± 7 yr) completed either the EG program (n = 15) or the TPE program (n = 14). Statistically significant group-time interactions were observed in Trail Making Test Part A (P < 0.05) and single-task gait speed, stride length, swing time percentage, and double support percentage (all P < 0.05), and marginal group differences were observed in Mini-Mental State Examination (P = 0.07), all favoring the EG program. There were no statistically significant group differences in dual-task gait measurements except for swing time percentage and double support percentage, favoring the EG program. CONCLUSIONS: An 8-wk EG program for older adults at risk for falls contributed to modest improvements in a number of cognitive measures and single-task but limited improvements in dual-task gait measures, compared with TPE. These findings support the need for larger trials to determine cognitive and mobility benefits related to EG.

Physiological responses and enjoyment of Kinect-based exergames in older adults at risk for falls: A feasibility study.

BACKGROUND: Exergaming has the potential to improve physical function, cognition and dual-task function, and could be an effective new strategy for reducing risk of falling in older adults. OBJECTIVE: To evaluate and test custom Microsoft Kinect-based motion-tracking exergames in older adults at risk for falls. METHODS: Community-dwelling older adults who reported mobility difficulties or had fallen in the past year played three newly developed exergames (Target Trackers, Double Decision, and Visual Sweeps, 5 minutes each) in random order. Heart rate (HR) was measured during, and blood pressures (BPs), rating of perceived exertion (RPE), and rating of the enjoyment were recorded immediately after each exergame. RESULTS: Seven participants (median age 75 y; 4 females) completed the study. There were no adverse events reported during the exergaming session. Exercise HRs and RPEs were statistically significantly higher than resting for all exergames (p< 0.05). The differences were not significant for BPs. Enjoyment ratings ranged from 79.6-90.6% and there were no statistically significant differences between the exergames. CONCLUSIONS: The newly developed exergames were light in exercise intensity and enjoyable for older adults at risk for falls. Future intervention studies are warranted to examine the benefits of exergames for this special population.

Functional Workspace Optimization via Learning Personal Preferences from Virtual Experiences.

The functionality of a workspace is one of the most important considerations in both virtual world design and interior design. To offer appropriate functionality to the user, designers usually take some general rules into account, e.g., general workflow and average stature of users, which are summarized from the population statistics. Yet, such general rules cannot reflect the personal preferences of a single individual, which vary from person to person. In this paper, we intend to optimize a functional workspace according to the personal preferences of the specific individual who will use it. We come up with an approach to learn the individual's personal preferences from his activities while using a virtual version of the workspace via virtual reality devices. Then, we construct a cost function, which incorporates personal preferences, spatial constraints, pose assessments, and visual field. At last, the cost function is optimized to achieve an optimal layout. To evaluate the approach, we experimented with different settings. The results of the user study show that the workspaces updated in this way better fit the users.

Fast and Scalable Position-Based Layout Synthesis.

The arrangement of objects into a layout can be challenging for non-experts, as is affirmed by the existence of interior design professionals. Recent research into the automation of this task has yielded methods that can synthesize layouts of objects respecting aesthetic and functional constraints that are non-linear and competing. These methods usually adopt a stochastic optimization scheme, which samples from different layout configurations, a process that is slow and inefficient. We introduce an physics-motivated, continuous layout synthesis technique, which results in a significant gain in speed and is readily scalable. We demonstrate our method on a variety of examples and show that it achieves results similar to conventional layout synthesis based on Markov chain Monte Carlo (McMC) state-search, but is faster by at least an order of magnitude and can handle layouts of unprecedented size as well as tightly-packed layouts that can overwhelm McMC.

A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation.

Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. Both the tool and dataset will be available at http://scenenn.net.

Exercise Intensity-Driven Level Design.

Games and experiences designed for virtual or augmented reality usually require the player to move physically to play. This poses substantial challenge for level designers because the player's physical experience in a level will need to be considered, otherwise the level may turn out to be too exhausting or not challenging enough. This paper presents a novel approach to optimize level designs by considering the physical challenge imposed upon the player in completing a level of motion-based games. A game level is represented as an assembly of chunks characterized by the exercise intensity levels they impose on players. We formulate game level synthesis as an optimization problem, where the chunks are assembled in a way to achieve an optimized level of intensity. To allow the synthesis of game levels of varying lengths, we solve the trans-dimensional optimization problem with a Reversible-jump Markov chain Monte Carlo technique. We demonstrate that our approach can be applied to generate game levels for s of motion-based virtual reality games. A user evaluation validates the effectiveness of our approach in generating levels with the desired amount of physical challenge.

Automatic Optimization of Wayfinding Design.

Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, as well as the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making mistakes during a navigation. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts such as a train station, a downtown and a canyon. We evaluate our results by comparing different wayfinding designs and show that our optimized wayfinding design can guide pedestrians to their destinations effectively and efficiently. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.

Earthquake Safety Training through Virtual Drills.

Recent popularity of consumer-grade virtual reality devices, such as the Oculus Rift and the HTC Vive, has enabled household users to experience highly immersive virtual environments. We take advantage of the commercial availability of these devices to provide an immersive and novel virtual reality training approach, designed to teach individuals how to survive earthquakes, in common indoor environments. Our approach makes use of virtual environments realistically populated with furniture objects for training. During a training, a virtual earthquake is simulated. The user navigates in, and manipulates with, the virtual environments to avoid getting hurt, while learning the observation and self-protection skills to survive an earthquake. We demonstrated our approach for common scene types such as offices, living rooms and dining rooms. To test the effectiveness of our approach, we conducted an evaluation by asking users to train in several rooms of a given scene type and then test in a new room of the same type. Evaluation results show that our virtual reality training approach is effective, with the participants who are trained by our approach performing better, on average, than those trained by alternative approaches in terms of the capabilities to avoid physical damage and to detect potentially dangerous objects.

The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes.

We introduce the Clutterpalette, an interactive tool for detailing indoor scenes with small-scale items. When the user points to a location in the scene, the Clutterpalette suggests detail items for that location. In order to present appropriate suggestions, the Clutterpalette is trained on a dataset of images of real-world scenes, annotated with support relations. Our experiments demonstrate that the adaptive suggestions presented by the Clutterpalette increase modeling speed and enhance the realism of indoor scenes.