Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar

We present a segmentation algorithm capable of segmenting exercise repetitions in real-time. This approach uses subsequence dynamic time warping and requires only a single exemplar repetition of an exercise to correctly segment repetitions from other subjects, including those with limited mobility. This approach is invariant to low range of motion, instability in movements and sensor noise while remaining selective to different exercises. This algorithm enables responsive feedback for technology-assisted physical rehabilitation systems. We evaluated the algorithm against a publicly available dataset (CMU) and against a healthy population and stroke patient population performing rehabilitation exercises captured on a consumer-level depth sensor. We show the algorithm can consistently achieve correct segmentation in real-time

iCarer Project: Intelligent Care Guidance and Learning Services Platform for Informal Carers of the Elderly

The increasing ageing population is demanding new care approaches to maintain the quality of life of elderly people. Informal carers are becoming crucial agents in the care and support of elderly people, which can lead to those carers suffering from additional stress due to competing priorities with employment or due to lack of knowledge about elderly people?s care needs. Thus, support and stress relief in carers should be a key issue in the home-care process of these older adults. Considering this context, this work presents the iCarer project aimed at developing a personalized and adaptive platform to offer informal carers support by means of monitoring their activities of daily care and psychological state, as well as providing an orientation to help them improve the care provided. Additionally, iCarer will provide e-Learning services and an informal carers learning network. As a result, carers will be able to expand their knowledge, supported by the experience provided by expert counsellors and fellow carers. Additionally, the coordination between formal and informal carers will be improved, offering the informal carers flexibility to organize and combine their assistance and social activities

Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applciations. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data

Human activity learning for assistive robotics using a classifier ensemble

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K-nearest neighbour and random forest - is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities

Pragmatic Design with Ada

This paper examines some of the problems associated with the design and implementation of large real-time systems in Ada. Methods for reducing these problems are described, including the identification of patterns within the behaviour of the system leading to the use of standardised patterns for the structure of the solutions. Case studies from real-life projects are used to illustrate these ideas. 1. Introduction Ada is normal ly considered the language of choice for large, complex real time systems, certainly if they have some safety, reliability or security requirement. Unfortunately such systems are prone to problems associated with their scale. This paper exam ines a few heuristics which may assist in solving these problems. 2. Problems in large projects The main problem with large systems is communications. Other risks - timescales, underestimating effort, performance are true in al l projects, but are compounded here by fai lures of communications. Typical aspects of the probl..

Human emotional understanding for empathetic companion robots

Companion robots are becoming more common in home environments, as such a greater emphasis is required on analysis of human behaviour. An important aspect of human behaviour is emotion, both the ability to express and comprehend. While humans have developed excellent skills in inferring the emotional states of their counterparts via implicit cues such as facial expression and body language, this level of understanding is often neglected in Human Robot Interactions; furthermore, humans are able to empathetically respond to the emotions of others to create amore harmonious and person relationship. This paper is a preliminary proposal of a novel approach for facial emotional detection and appropriate empathetic responses, in conjunction with long term emotion mapping and prediction; the proposed system will be implemented on a social mobile robot, thus allowing a further level of behavioural comprehension to achieve a more human like encounter. The technique will be based on Fuzzy Cognitive Maps, using FACS Action Units as inputs, a high level facial descriptor layer and output of six emotions. © Springer International Publishing AG 2017