Healthy Ageing represents one of the most crucial topics that our society will face in the next decades, due to the progressive demographic ageing of the population, (e.g., in Italy old-age dependency ratio is estimated to reach 59,7% by 2065). Vehicle feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle system state to users. Moving forward, it will be necessary for HAV systems to keep users in-the-loop in an effort to increase system transparency and overall understanding. In addition, several key behavioral traits are identified as factors that contributed to users’ baseline comfort levels with the HAV. A considerable link was found between HMI modality and users’ reported levels of comfort, safety, and trust during experimentation. To better understand user trust and perceptions of comfort and safety while riding in an HAV, this study evaluated in-situ human-machine interface (HMI) systems (visual, auditory, and mixed-modal) to relay vehicle “intentions” (e.g., the vehicle's response to roadway stimuli) to the user. Research suggests the general public has inherent distrust in highly automated vehicles (HAV), typically stemming from a lack of vehicle system transparency while in motion (e.g., the user not being informed how the car will react in the upcoming scene) and not having an effective way to control the vehicle in the event of a system failure. Results indicate that healthcare professionals focused more on the assistive aspects, whereas care partners focused more on the social aspects of the SAR. However, participants’ perceptions varied by task. Both groups expressed willingness to adopt such technology and found that it could be useful in dementia care. Six use cases involving a SAR (NAO, SoftBank) were demonstrated to both caregiver groups (N=20 persons). This qualitative study investigated and compared both caregiver groups’ acceptance of a SAR. It is crucial to understand the divergent tasks of these two caregiver groups so that the SAR’s intervention can meet each group’s needs. To address the rising demand on healthcare professionals and informal care partners of PWD, socially assistive robots (SARs) can potentially facilitate care provision. Yet, at the same time, there are fewer health care professionals per care recipient. As a first step in the prototyping of the system, we were able to control two separate game interfaces developed using Unit圓D software.Īs the older population increases, the number of persons living with dementia (PWD) will increase as well. We were able to achieve a reliable data transfer between the database and the different input acquisition interface. They capture the user’s tactile input, vocal phrases, eye gaze as well as head pose features like tilt and face direction. The user input is acquired using a smartphone and a webcam equipped computer. This system relies on a web based database called Firebase for the exchange of user input and the issuing of commands to the multiple artifacts. In this paper, we present the general architecture of an ongoing project for multimodal home automation system.
This is especially true if the user needs to interact with multiple artifacts in a home automation environment. Including many modalities can rapidly increase the number of interaction objects that need to receive the stream of user commands. The solution then becomes the inclusion of multiple modalities in the initial design of the interactive system making it more adaptable to the needs of many more users. Relying on one technology with a single interaction modality may benefit some users but would certainly exclude a lot more if they have impedances to use that modality.
The measured levels of satisfaction and continuance intention to use provide promising results to reuse our DPs and further develop our prototype for hybrid collaborative learning. We found that the prototype following the DPs successfully contributed to positive learning effects as well as a high continuance intention to use. The prototype was evaluated with 31 expert and 30 novice customer service employees of an organization. For implementation and evaluation, we selected a customer service use case as a top domain of research on AI applications. To design such a HIS, we implemented a prototype based on formulated design principles (DPs) to teach and learn from its human user while collaborating on a task. They particularly allow the combination of human-in-the-loop and computer-in-the-loop learning ensuring a hybrid collaborative learning cycle. Hybrid intelligence systems (HIS) enable human users and Artificial Intelligence (AI) to collaborate in activities complementing each other.
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