Background: Apathy is common among older adults residing in long-term care (LTC) and impairs quality of life for both older adults and care providers. Few pharmacological remedies exist, and nonpharmacologic approaches that engage those with apathy require extensive personnel time. Thus, technological approaches have been encouraged, including virtual reality (VR) and socially assistive robots (SAR). Despite a growing interest in their use, input from older adults and staff is often absent in their design. Involving older adults in the development of interactive health technologies is necessary to enhance the functionality, usability, and likelihood of promoting the intended health outcomes. Objective: We aimed to design and evaluate SAR and nonimmersive VR (SAR-VR) activities for pairs of older adults that would encourage human-to-human interaction, an essential activity to mitigate apathy. Methods: We implemented a multistep, user-centered design. A humanoid and dog SAR were used in combination with nonimmersive VR activities for pairs of older adults. An interdisciplinary team of engineers, nurses, and physicians collaborated with older adults and staff to create 4 activity prototypes, 3 with the humanoid robot and 1 with the dog robot. A total of 14 older adults at 2 sites participated in the design and evaluation of the different components of the system throughout all stages. Site 1 participants were instrumental in the development, and Site 2 participants validated the prototype activities. Data were collected at each session via observations, interviews, and a 6-item questionnaire that rated their degree of comfort and confidence in (1) using the wands, (2) interacting with the robot, and (3) interacting with the nonimmersive VR environment using a 5-point Likert response. Additionally, 5 staff from Site 2 were recruited to evaluate the ease of setting up and running the system at 2 different sessions. After each session, the system setup and interface were refined based on their feedback. Results: A total of 4 of 6 older adults (mean age 85, SD 9.3 years; 2 male) at Site 1 completed field testing development, and 8 residents (mean age 80, SD 4.7 years; 2 male) at Site 2 completed field testing validation. Participant comfort and confidence increased significantly over successive iterations of the system across most categories (Site 1: Wilcoxon signed rank test P=.03; Site 2: Wilcoxon signed rank test P<.001). Additionally, 5 LTC staff members successfully set up the system with minimal cueing from the researchers, demonstrating the usability of the system for caregivers. Iterative design changes incorporated hardware, software, and activity domains. Conclusions: These initial results demonstrate that LTC older adults and staff are capable and critical to the development and implementation of SAR-VR activities. Future studies are needed to evaluate the feasibility of implementation and effectiveness in reducing apathy. Trial Registration: ClinicalTrials.gov NCT05178992; https://clinicaltrials.gov/study/NCT05178992

Research on Brain-Computer Interface (BCI) began in the 1970s and has increased in volume and diversified significantly since then. Today BCI is widely used for applications like assistive devices for physically challenged users, mental state monitoring, input devices for hands-free applications, marketing, education, security, games and entertainment. This article explores the advantages and disadvantages of invasive and non-invasive BCI technologies and focuses on use cases of several non-invasive technologies, namely electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), Near Infrared Spectroscopy (NIRs) and hybrid systems.

In this study, we designed and validated the Multi-user Intelligent Dynamic Difficulty Adjustment System (MIDDAS), a hierarchical, task-independent architecture for single-user and multi-user cooperative tasks. MIDDAS consists of an individual DDA module for each user and a shared interaction manager. Each DDA module uses a partially observable Markov decision process (POMDP) to adjust task parameters while accounting for uncertainty in estimation of user-states. The interaction manager uses a Markov decision process (MDP) to regulate interaction between users. Both components separate task-dependent data-processing and action layers from a generalized decision-making layer. Synthetic data was used to train the decision-making policies. Data from ten human participants were used to calibrate the neural network models that generated the synthetic data. The trained policies were compared with hand-crafted state-machines using ten synthetic participants and five synthetic participant pairs. The trained policy performed marginally better than the hand-crafted state-machines, while the POMDP policy showed an increasing performance advantage over the hand-crafted policy as state-estimation accuracy declined. These results support the feasibility of MIDDAS and the usefulness of the trained policies as a starter policy for subsequent fine-tuning using real-time participant gameplay data.

Affective states regulate our day to day to function and has a tremendous effect on mental and physical health. Detection of affective states is of utmost importance for mental health monitoring, smart entertainment selection and dynamic workload management. In this paper, we discussed relevant literature on affective state detection using physiology data, the benefits and limitations of different sensors and methods used for collecting physiology data, and our rationale for selecting functional near-infrared spectroscopy. We present the design of an experiment involving nine subjects to evoke the affective states of meditation, amusement and cognitive load and the results of the attempt to classify using machine learning. A mean accuracy of 83.04% was achieved in three class classification with an individual model; 84.39% accuracy was achieved for a group model and 60.57% accuracy was achieved for subject independent model using leave one out cross validation. It was found that prediction accuracy for cognitive load was higher (evoked using a pen and paper task) than the other two classes (evoked using computer bases tasks). To verify that this discrepancy was not due to motor skills involved in the pen and paper task, a second experiment was conducted using four participants and the results of that experiment has also been presented in the paper.