What to Put on the User

Abstract

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: PPGbased 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 Design Space for Wearables in Research Settings and reflected on the appropriateness of the investigated technologies in research contexts.

“Feeling the Sensor Feeling you”: A Soma Design Exploration on Sensing Non-habitual Breathing

“…Such approaches to working with sensing technologies are diferent compared to using biosensors in a lab or in stationary settings. In such settings, factors such as strictly following the suggested placement of a sensor on the body or keeping the body static by restricting movements, are crucial for capturing the type of biodata a particular sensor has been designed for [23]. But, in reality, design researchers and sometimes even end-users [5], often end up adapting biosensors to ft a particular design context and even diferent bodies that will be sensed.…”

Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes

“…Wearables are often equipped with multiple sensors to collect physiological data. For example, there are wrist-based consumer smartwatches which provide data such as interbeat intervals recorded by an optical sensor (see Figure 2), three-axis accelerometer data, step count, burned calories, or proprietary stress values [6].…”