People with language impairments can have difficulties expressing themselves to others, leading to major limitations to their safety, independence, and quality of life in general. Aphasia is an example of an acquired language impairment that affects many people (around 2 million in the United States), being commonly caused by stroke, but also by other brain injuries. Several augmentative and alternative communication solutions are available to help people with communication difficulties, but they are generally not suitable for all contexts of use (e.g., lying in bed). In the scope of the “APH-ALARM” project, which aimed at developing solutions to support people with Aphasia, we envision a system for the bedroom that enables conveying messages to be sent to a caregiver or relative, for example. Focusing on gesture input, in this contribution, we investigated if smartwatch sensors and machine learning (ML) can be used to recognise arm gestures executed while lying. We explored different factors, namely the feature set, size of the sliding window used for feature extraction, and ML classifier. The results obtained with data gathered from ten subjects are promising, with the best factor combinations for the user-independent solution leading to a mean macro F1 score of 94% or 95%. They demonstrate the potential of using wearables to develop a gesture input modality for the in-bed scenario, which can also potentially be extended to other contexts (e.g., sitting in a bed, chair, or sofa, or standing). This research also provides useful insights that inform future work, including the development and deployment of communication support systems that can benefit not only people with communication difficulties (e.g., more independence), but also those caring for them (e.g., more peace of mind). • A new gesture-based interaction modality for communication support is proposed. • The proposal is aligned with the needs of users with Aphasia while lying in bed. • The use of wearable sensors for arm gesture recognition is investigated. • The best achieved mean F1 score was of 95%, for a user-independent solution.

Social anxiety is significantly prevalent among college students and frequently leads to school dropout and academic underachievement. Since university counseling services are often overburdened, a mobile-based cognitive behavioral intervention was designed to support students attending therapy for social anxiety and therapists. Our main goals are to evaluate usability and acceptability of this technological system developed by a multidisciplinary team. Twenty students and three therapists participated in the usability studies. To evaluate usability we utilized a thinking aloud and question asking protocol, and the system usability scale (SUS) to assess usability and satisfaction. SPICA app demonstrated usability scores above average and the large majority of students agreed that the app was easy to use and that they would use it frequently. As for the therapist platform (database) preliminary results indicated that the database is useful but some alterations are still necessary. SPICA app demonstrated usability and acceptability among college students. A more rigorous study is needed to assess the therapist platform but preliminary results demonstrate acceptability.

We present a Cyber-Physical System for distributed monitoring of first response teams. To cope with different setups, different sensors and computing devices, found in the evolution of the VitalResponder project, we adopted a message brokering pattern to isolate the data aggregation logic from the c

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Professionals such as First Responders are frequently exposed to extreme environmental conditions, which induce stress and fatigue during extensive periods of time. In this scenario, the main issues are the quantification and evaluation of stress and fatigue, since uncontrolled levels have a profound and negative impact on human health and performance. Based on an existing wearable monitoring solution - the Vital Jacket®- we propose an individual and team monitoring mobile solution called DroidJacket. DroidJacket is based on Android mobile devices and provides data aggregation, processing, visualization and optionally relaying services. The DroidJacket design is plugin oriented, integrating analysis modules, namely an online ECG plugin for both real time pulse and arrhythmia detection.