A Social Robot, “Dali,” for Aging in Place Technology

Healthcare has a trend of going hi-tech. With an aging population growing more than ever, researchers and health care providers are now relying on robots to ease the symptoms of dementia and help an aging population stay where they would like, at home. Several therapeutic robots such as Paro recently introduced in the markets are manifestation of such trends. In this paper, we propose a social robot missioned to autonomously capture images of people and feed multimedia contents to a social network or to a hospital for various social activities or for health monitoring purpose. The main technical barriers of such robots include autonomous navigation, human face detection, distance, and angle adjustment for clean and better shots. To that end, we study autonomous mapping/navigation as well as optimal image capturing technology via motion planning and visual servoing. To overcome the mapping and navigation at a crowded environment, we use the potential field path planning harnessed with two competitive potential update techniques. The robot is an agent navigating in a potential field where detected environmental significances provide sources of attractive forces, while previously occupied locations estimated by SLAM technique provide sources of repelling forces. We also study visual servo technique to optimize image capturing processes. This includes facial recognition, photographic distance/angle adjustment, and backlight avoidance. We tested several scenarios with the assembled robot for its usefulness.

An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics

Purpose This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters. Design/methodology/approach In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data. Findings There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation. Originality/value In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.

Robot Friends for Autistic Children

Robots seem to have great therapeutic value for patients with autism spectrum disorders. But their usefulness derives from a potentially problematic source: their appealingly friendly presence, which can lead patients to think of them as friends, or even to prefer their companionship to that of human beings. In this chapter, an analogy between false friends and counterfeit currency is leveraged to explore a potential moral hazard posed by these therapeutic robots. An objection from the subjective nature of the value of friendship is raised, and refuted by an appeal to the importance of cultivating social capabilities. I conclude that the moral hazard can be mitigated by careful design and responsible use, and that these therapies offer genuine promise. But I argue that we must tread with caution when using robots in therapeutic applications where the appearance of friendship is liable to arise.

Technological Tools and Interventions to Enhance Learning in Children with Autism

Recent years have witnessed an alarming rise in the number of children diagnosed with Autism Spectrum Disorder (ASD). These children have special needs and hence require different kind of learning mechanisms as well as access to technological interventions that offer extra means of building links for an individual. This heightened focus includes services and interventions combined with technological advances that redefine how support and instruction can be provided. This chapter presents an overview of emerging technology tools such as Virtual Environment (VE)/Collaborative Virtual Environment (CVE), therapeutic robots, language tools, multimedia handheld devices, floor/table top projectors along with different interventions that have been used to enhance different learning skills in children with ASD.

Enhancement of the Communication Effectiveness of Interactive Robots for Autism Therapy by Using Touch and Colour Feedback

Previous studies in the field of robot assisted therapy demonstrated that robots engage autistic children’s attention in a better way. Therefore, the interactive robots appear to be a promising approach for improving the social interaction and communication skills of autistic children. However, most of the existing interactive robots use a very small number of communication variableswhich narrow their effectiveness to a few aspects of autistic childrens’ social communication behaviour. In the present work, we explore the effects of touching and colours on the communication effectiveness between a robot and an autistic child and their potential for further adjustability of the robot to child’s behaviour. Firstly, we investigated touching patterns of autistic and non-autistic children in three different situations and validated their responses by comparison of touching forces. Results showed that patterns of touching by non-autistic children have certain consistency, while reaction patterns in autistic children vary from person to person. Secondly, we studied the effect of colour feedback in autism therapy with the robot. Results showed that participants achieved better completion rate when colour feedback was provided. The results could support the design of more effective therapeutic robots for children with autism.