Motion-Based Video Games for Stroke Rehabilitation with Reduced Compensatory Motions

Stroke is the leading cause of long-term disability among adults in industrialized nations, with 80% of people who survive strokes experiencing motor disabilities. Recovery requires daily exercise with a high number of repetitions, often without therapist supervision. Motion-based video games can help motivate people with stroke to perform the necessary exercises to recover. We explore the design space of video games for stroke rehabilitation using Wii remotes and webcams as input devices, and share the lessons we learned about what makes games therapeutically useful. We demonstrate the feasibility of using games for home-based stroke therapy with a six-week case study. We show that exercise with games can help recovery even 17 years after the stroke, and share the lessons that we learned for game systems to be used at home as a part of outpatient therapy. As a major issue with home-based therapy, we identify that unsupervised exercises lead to compensatory motions that can impede recovery and create new health issues. We reliably detect torso compensation in shoulder exercises using a custom harness, and develop a game that meaningfully uses both exercise and compensation as inputs. We provide in-game feedback that reduces compensation in a number of ways. We evaluate alternative ways for reducing compensation in controlled experiments and show that using techniques from operant conditioning are effective in significantly reducing compensatory behavior compared to existing approaches

Combined Controllers that Follow Imperfect Input Motions for Humanoid Robots

Humanoid robots have the potential to become a part of everyday life as their hardware and software challenges are being solved. In this paper we present a system that gets as input a motion trajectory in the form of motion capture data, and produces a controller that controls a humanoid robot in real-time to achieve a motion trajectory that is similar to the input motion data. The controller expects the input motion data not to be dynamically feasible for the robot and employs a combined controller with corrective components to keep the robot balanced while following the motion. Since the system can run in real-time, it can be thought of a candidate for teleoperation of humanoid robots using motion capture hardware

Adaptive Embedded Roadmaps for Sensor Networks

In this paper, we propose a new approach to wireless sensor network assisted navigation while avoiding moving dangers. Our approach relies on an embedded roadmap in the sensor network that always contains safe paths. The roadmap is adaptive, i.e., it adapts its topology to changing dangers. The mobile robots in the environment uses the roadmap to reach their destinations. We evaluated the performance of embedded roadmap both in simulations using realistic conditions and with real hardware. Our results show that the proposed navigation algorithm is better suited for sensor networks than traditional navigation field based algorithms. Our observations suggest that there are two drawbacks of traditional navigation field based algorithms, (i) increased power consumption, (ii) message congestion that can prevent important danger avoidance messages to be received by the robots. In contrast, our approach significantly reduces the number of messages on the network (up to 160 times in some scenarios) and power consumption while increasing the navigation performance

Automated Motion Synthesis for Virtual Choreography

In this paper, we present a technique to automati-cally synthesize dancing moves for arbitrary songs. Our current implementation is for virtual characters, but it is easy to use the same algorithms for entertainer robots, such as robotic dancers, which fits very well to this year’s conference theme. Our technique is based on analyzing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the character’s movement synchronizes to the musical beats. In order to analyze beats of the tune, we developed a fast and novel algorithm. Our motion synthesis algorithm analyze library of stock motions and generates new sequences of movements that were not described in the library. We present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music

216 Inhibition of brutons tyrosine kinase (BTK) prevents inflammatory macrophage differentiation: a potential role in SLE

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The development of a video retrieval system using a clinician-led approach

Patient video taken at home can provide valuable insights into the recovery progress during a programme of physical therapy, but is very time consuming for clinician review. Our work focussed on (i) enabling any patient to share information about progress at home, simply by sharing video and (ii) building intelligent systems to support Physical Therapists (PTs) in reviewing this video data and extracting the necessary detail. This paper reports the development of the system, appropriate for future clinical use without reliance on a technical team, and the clinician involvement in that development. We contribute an interactive content-based video retrieval system that significantly reduces the time taken for clinicians to review videos, using human head movement as an example. The system supports query-by-movement (clinicians move their own body to define search queries) and retrieves the essential fine-grained movements needed for clinical interpretation. This is done by comparing sequences of image-based pose estimates (here head rotations) through a distance metric (here Fréchet distance) and presenting a ranked list of similar movements to clinicians for review. In contrast to existing intelligent systems for retrospective review of human movement, the system supports a flexible analysis where clinicians can look for any movement that interests them. Evaluation by a group of PTs with expertise in training movement control showed that 96% of all relevant movements were identified with time savings of as much as 99.1% compared to reviewing target videos in full. The novelty of this contribution includes retrospective progress monitoring that preserves context through video, and content-based video retrieval that supports both fine-grained human actions and query-by-movement. Future research, including large clinician-led studies, will refine the technical aspects and explore the benefits in terms of patient outcomes, PT time, and financial savings over the course of a programme of therapy. It is anticipated that this clinician-led approach will mitigate the reported slow clinical uptake of technology with resulting patient benefit

Data S1: User-study-raw-data

Background. Regular physical activity can substantially improve the physical wellbeing of older adults, preventing several chronic diseases and increasing cognitive performance and mood. However, research has shown that older adults are the most sedentary segment of society, spending much of their time seated or inactive. A variety of barriers make it difficult for older adults to maintain an active lifestyle, including logistical difficulties in going to a gym (for some adults, leaving home can be challenging), reduced functional abilities, and lack of motivation. In this paper, we report on the design and evaluation of Gymcentral. A training application running on tablet was designed to allow older adults to follow a personalized home-based exercise program while being remotely assisted by a coach. The objective of the study was to assess if a virtual gym that enables virtual presence and social interaction is more motivating for training than the same virtual gym without social interaction.Methods. A total of 37 adults aged between 65 and 87 years old (28 females and 9 males, mean age = 71, sd = 5.8) followed a personalized home-based strength and balance training plan for eight weeks. The participants performed the exercises autonomously at home using the Gymcentral application. Participants were assigned to two training groups: the Social group used an application with persuasive and social functionalities, while the Control group used a basic version of the service with no persuasive and social features. We further explored the effects of social facilitation, and in particular of virtual social presence, in user participation to training sessions. Outcome measures were adherence, persistence and co-presence rate.Results. Participants in the Social group attended significantly more exercise sessions than the Control group, providing evidence of a better engagement in the training program. Besides the focus on social persuasion measures, the study also confirms that a virtual gym service is effective for supporting individually tailored home-based physical training for older adults. The study also confirms that social facilitation tools motivate users to train together in a virtual fitness environment.Discussion. The study confirms that Gymcentral increases the participation of older adults in physical training compare to a similar version of the application without social and persuasive features. In addition, a significant increase in the co-presence of the Social group indicates that social presence motivates the participants to join training sessions at the same time with the other participants. These results are encouraging, as they motivate further research into using home-based training programs as an opportunity to stay physically and socially active, especially for those who for various reasons are bound to stay at home

Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays

This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed a server based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS- SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross validated LS- SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation

A versatile turning centre with multi-microprocessor control

Imperial Users onl