Sleep is an important aspect of our health, but it is difficult for people to track manually because it is an unconscious activity. The ability to sense sleep has aimed to lower the barriers of tracking sleep. Although sleep sensors are widely available, their usefulness and potential to promote healthy sleep behaviors has not been fully realized. To understand people's perspectives on sleep sensing devices and their potential for promoting sleep health, we surveyed 87 and interviewed 12 people who currently use or have previously used sleep sensors, interviewed 5 sleep medical experts, and conducted an in-depth qualitative analysis of 6986 reviews of the most popular commercial sleep sensing technologies. We found that the feedback provided by current sleep sensing technologies affects users' perceptions of their sleep and encourages goals that are in tension with evidence-based methods for promoting good sleep health. Our research provides design recommendations for improving the feedback of sleep sensing technologies by bridging the gap between expert and user goals.

Serious brain injuries, spinal injuries, and motor neuron diseases often lead to severe paralysis. Individuals with such disabilities can benefit from interaction techniques that enable them to interact with the devices and thereby the world around them. While a number of systems have proposed tongue-based gesture detection systems, most of these systems require intrusive instrumentation of the user's body (e.g., tongue piercing, dental retainers, multiple electrodes on chin). In this paper, we propose a wireless, non-intrusive and non-contact facial gesture detection system using X-band Doppler. The system can accurately differentiate between 8 different facial gestures through non-contact sensing, with an average accuracy of 94.3%.

In this paper, we present DoppleSleep -- a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user's body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smartphone application that are benchmarked in terms of power and resource usage.

Sensing respiration rate has many applications in monitoring various health conditions, such as sleep apnea and chronic obstructive pulmonary disease. In this paper, we present WiBreathe, a wireless, high fidelity and non-invasive breathing monitor that leverages wireless signals at 2.4 GHz to estimate an individual's respiration rate. Our work extends past approaches of using wireless signals for respiratory monitoring by using only a single transmitter-receiver pair at the same frequency range of commodity Wi-Fi signals to estimate the respiratory rate of an individual. This is done irrespective of whether they are in line of sight or not (e.g., through walls). Furthermore, we demonstrate the capability of WiBreathe in detecting multiple people and by extension, their respiration rates. We evaluate our approach in various natural environments and show that we can track breathing with the accuracy of 1.54 breaths per minute when compared to a clinical respiratory chest band.