We focus on the stories of five older adults who became voice assistant users through our study, and with whom we speculated about future interfaces through two design probes, one for health data reporting and one for positive reminiscing. We delivered a voice-first ambient interface (VFAI) to each participant, and closely observed participants’ journeys through periodic themed interviews (16 hours, 21 minutes of transcribed recordings), usage log reviews (4,657 entries), and phone and text support. Participants’ lived experiences impacted their perceptions and interactions with their VFAI, fueling rich insights about how to design for diverse needs. For example, while one participant saw increased potential in the VFAI after interacting with the design probe for health data reporting, another was skeptical of using it to communicate with her doctor. We contribute an in-depth exploration of VFAIs to support aging in place, implications for design, and areas for future work for tailoring VFAIs towards enabling continuity of care in people’s homes.

Fitness trackers not just provide easy means to acquire physiological data in real-world environments due to affordable sensing technologies, they further offer opportunities for physiology-aware applications and studies in HCI; however, their performance is not well understood. In this paper, we report findings on the quality of 3 sensing technologies: PPG-based wrist trackers (Apple Watch, Microsoft Band 2), an ECG-belt (Polar H7) and reference device with stick-on ECG electrodes (Nexus 10). We collected physiological (heart rate, electrodermal activity, skin temperature) and subjective data from 21 participants performing combinations of physical activity and stressful tasks. Our empirical research indicates that wrist devices provide a good sensing performance in stationary settings. However, they lack accuracy when participants are mobile or if tasks require physical activity. Based on our findings, we suggest a textitDesign Space for Wearables in Research Settings and reflected on the appropriateness of the investigated technologies in research contexts.

We present a framework - AWSense - for eased sensing data collection from an Apple Watch wearable device. The framework eases the access, transmission and export of sensing data from the device. This data comprises: heart rate, raw acceleration, and computed device motion. In our demo, we present sample applications built on top of this framework to show its capabilities, of real-time presentation and recording of the sensing data.