Animallike robot companions such as robotic seal Paro are increasingly used in dementia care due to the positive effects that interaction with these robots can have on the well-being of these patients. Touch is one of the most important interaction modalities for patients with dementia and can be a natural way to interact with animallike robots. To advance the development of animallike robots we explored in what ways people with dementia could benefit from interaction with an animallike robot with more advanced touch recognition capabilities and which touch gestures would be important in their interaction with Paro. In addition we explored which other target groups might benefit from interaction with animallike robots with more advanced interaction capabilties. In this study we administered a questionnaire and conducted interviews with two groups of health care providers who all worked in a geriatric psychiatry department. One group used Paro in their work (i.e., the expert group; $n=5$) while the other group had no experience with the use of animallike robot (i.e., the layman group; $n=4$). The results showed that health care providers perceived Paro as an effective intervention to improve the well-being of people with dementia. Examples of usages for Paro that were mentioned were providing distraction, interrupting problematic behaviors and stimulating communication. Furthermore, the care providers indicated that people with dementia (would) use mostly positive forms of touch and speech to interact with Paro. Paro's auditory responses were criticized because they can overstimulate the patients. Additionally, the care providers argued that social interactions with Paro are currently limited and therefore the robot does not meet the needs of a broader audience such as healthy elderly people that still live in their own homes. The development of robot pets with more advanced social capabilities such as touch and speech recognition might result in more intelligent interactions which could help to better adapt to the needs of people with dementia and could make interactions more interesting for a broader audience. Moreover, the robot's response modalities and its appearance should match the needs of to the target group.

For an artifact such as a robot or a virtual agent to respond appropriately to human social touch behavior, it should be able to automatically detect and recognize touch. This paper describes the data collection of CoST: Corpus of Social Touch, a data set containing 7805 captures of 14 different social touch gestures. All touch gestures were performed in three variants: gentle, normal and rough on a pressure sensor grid wrapped around a mannequin arm. Recognition of these 14 gesture classes using various classifiers yielded accuracies up to 60 %; moreover, gentle gestures proved to be harder to classify than normal and rough gestures. We further investigated how different classifiers, interpersonal differences, gesture confusions and gesture variants affected the recognition accuracy. Finally, we present directions for further research to ensure proper transfer of the touch modality from interpersonal interaction to areas such as human–robot interaction (HRI).

Touch behavior is of great importance during social interaction. To transfer the tactile modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI) and remote communication automatic recognition of social touch is necessary. This paper introduces CoST: Corpus of Social Touch, a collection containing 7805 instances of 14 different social touch gestures. The gestures were performed in three variations: gentle, normal and rough, on a sensor grid wrapped around a mannequin arm. Recognition of the rough variations of these 14 gesture classes using Bayesian classifiers and Support Vector Machines (SVMs) resulted in an overall accuracy of 54% and 53%, respectively. Furthermore, this paper provides more insight into the challenges of automatic recognition of social touch gestures, including which gestures can be recognized more easily and which are more difficult to recognize.

Mobility impairments can prevent older adults from performing their daily activities, which highly impacts quality of life. Powered exoskeletons, which are wearable robotic devices, can assist older adults by providing additional support to compensate for age-related decline in muscle strength. To date, little is known about the opinions and needs of older adults regarding exoskeletons, as current research primarily focuses on the technical development of exoskeleton devices and on groups with more severe mobility impairments such as people with spinal cord injuries. Therefore, the aim of this article is to inform the design of exoskeletons for older adults from a person-centered perspective. Interviews were conducted with seven older adults and six clinicians. Results indicated that exoskeletons can be a valuable addition to existing mobility devices to support older adults during walking and other actions necessary to perform their daily activities. Although reactions towards the concept of exoskeleton technology were positive, older adults did not currently perceive the need for an exoskeleton device. Exoskeletons for older adults should be easy to use; preferably, users should be able to put the device on and take it off independently. Moreover, the appearance of the exoskeleton should be as inconspicuous as possible, as most older adults do not like to advertise their need for assistive devices. At this point in time, the willingness to use exoskeleton technology will depend on personal needs and preferences.

Advances in the field of touch recognition could open up applications for touch-based interaction in areas such as Human-Robot Interaction (HRI). We extended this challenge to the research community working on multimodal interaction with the goal of sparking interest in the touch modality and to promote exploration of the use of data processing techniques from other more mature modalities for touch recognition. Two data sets were made available containing labeled pressure sensor data of social touch gestures that were performed by touching a touch-sensitive surface with the hand. Each set was collected from similar sensor grids, but under conditions reflecting different application orientations: CoST: Corpus of Social Touch and HAART: The Human-Animal Affective Robot Touch gesture set. In this paper we describe the challenge protocol and summa- rize the results from the touch challenge hosted in conjunction with the 2015 ACM International Conference on Multi- modal Interaction (ICMI). The most important outcomes of the challenges were: (1) transferring techniques from other modalities, such as image processing, speech, and human action recognition provided valuable feature sets; (2) gesture classification confusions were similar despite the various data processing methods used. Categorie