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We defined tolerance range as the distance of observing similar disease conditions or functional status from the upper to the lower boundaries of a specified time interval. A tolerance range was identified for linear regression and support vector machines to optimize the improvement rate (defined as IR) on accuracy in predicting mortality risk in patients with chronic obstructive pulmonary disease using clinical notes. The corpus includes pulmonary, cardiology, and radiology reports of 15,500 patients who died between 2011 and 2017. Their performance was compared against a long short-term memory recurrent neural network. The results demonstrate an overall improvement by those basic machine learning approaches after considering an optimal tolerance range: the average IR of linear regression was 90.1% and the maximum IR of support vector machines was 66.2%. There was a similitude between the time segments produced by our tolerance algorithms and those produced by the long short-term memory. [ABSTRACT FROM AUTHOR]

Effective route planning is the key to improving transportation efficiency. By leveraging the in-depth knowledge of road topology and traffic trends, experienced drivers (e.g., taxi drivers) can usually find near-optimal routes. However, existing online route planning services can hardly acquire this domain knowledge, so they just provide the fastest/shortest route based on current traffic conditions. These seemingly optimal routes may attract numerous vehicles and then become extremely congested. To solve this problem and actually improve transportation efficiency, we propose a double rewarded value iteration network (VIN) to fully learn the experienced drivers’ routing decisions, which are based on their implicitly estimated traffic trends. First, the global traffic status and routing actions are chronologically extracted from large-scale taxicab trajectories. Then, to model the knowledge of traffic trends, a long short-term memory network is trained. Being expert at learning long-term planning involved functions, the VIN is utilized to model the policy function from both current and predicted future traffic status to an experienced driver's routing action. Finally, the performance of our proposed model is evaluated on real map and taxicab trajectories in Beijing, China. The experimental results demonstrate that the proposed model can achieve human like performance in most cases, with high success rate and less commuting time. [ABSTRACT FROM AUTHOR]

Head-neck interfaces have the potential to command and control orientation tasks when the hand-wrist is not available for use as a joystick. We pose the question in this paper–How well can the head-neck be used to perform orientation tasks when compared to the hand-wrist? Anatomically, the motion of the head-neck is similar to that of the hand-wrist. We hypothesize that the head-neck motion can be as effective as the motion of the hand-wrist to control orientation tasks. A study was designed to characterize the ability of head-neck to command and control general orientation tasks. Fourteen healthy participants were asked to control the head orientation of an avatar on a computer screen using the motion of their head-neck and hand-wrist, measured by a robotic neck brace and a conventional joystick, respectively. Visual feedback was given to the participants with the display of the target and the actual head orientations of the avatar. The outcomes were defined for comparison between the head-neck and hand-wrist motions as follows: 1) mean absolute error; 2) time delay in tracking continuous orientation trajectories; and 3) settling time to reach target orientations. The results showed that the performance outcomes were significantly better with the hand-wrist than that of the head-neck when used as a joystick. However, all participants successfully completed the tasks with the head-neck. This demonstrates that the head-neck can be used as a joystick for controlling three dimensional object orientations, even though it may not be as dexterous as the hand-wrist. These results have fundamental implications in the design of devices and interfaces with the human head-neck. [ABSTRACT FROM AUTHOR]

In this study, we showed a way of reducing the metabolic cost of walking with a backpack using load distribution and dynamic load compensation, provided by a wearable upper body device. This device distributes the backpackload between the shouldersand the pelvis, senses the vertical motion of the pelvis, and provides gait synchronized compensatory forces to reduce the dynamic loads from a backpack. It was hypothesized that by reducing dynamic loads from a backpack during load carriage, the user’s gait and postural adaptation, muscular effort and metabolic cost would be reduced. This hypothesis was supported by biomechanical and physiological measurements on a group of young healthy subjects, as they walked on a treadmill under four different conditions: unloaded; with a backpack, loaded with 25% of their body weight, supported on the shoulders; with the same load distributed between the shoulders and the pelvis; and with dynamic load compensation in addition to load distribution. The results showed reductions in gait and postural adaptations, muscle activity, vertical and braking ground reaction forces, and metabolic cost while carrying the same backpack load with the device. We conclude that the device can potentially reduce the risk of musculoskeletal injuries and muscle fatigue associated with carrying heavy backpack loads while reducing the metabolic cost of loaded walking. [ABSTRACT FROM AUTHOR]

Neck muscle weakness due to amyotrophic lateral sclerosis (ALS) can result in dropped head syndrome, adversely impacting the quality of life of those affected. Static neck collars are currently prescribed to hold the head in a fixed upright position. However, these braces are uncomfortable and do not allow any voluntary head-neck movements. By contrast, powered neck exoskeletons have the potential to enable head-neck movements. Our group has recently improved the mechanical structure of a state-of-the-art neck exoskeleton through a weighted optimization. To evaluate the effect of the structural changes, we conducted an experiment in which patients with ALS were asked to perform head-neck tracking tasks while using the two versions of the neck exoskeleton. We found that the neck muscle activation was significantly reduced when assisted by the structurally enhanced design compared to no assistance provided. The improved structure also improved kinematics tracking performance, allowing users to better achieve the desired head poses. In comparison, the previous design did not help reduce the muscle effort required to perform these tasks and even slightly worsened the kinematic tracking performance. It was also found that biomechanical benefits gained from using the structurally improved design were consistent across participants with both mild and severe neck weakness. Furthermore, we observed that participants preferred to use the powered neck exoskeletons to voluntarily move their heads and make eye contact during a conversation task rather than remain in a fixed upright position. Each of these findings highlights the importance of the structural design of neck exoskeletons in achieving desired biomechanical benefits and suggests that neck exoskeletons can be a viable method to improve the daily life of patients with ALS.