Alexander Travis Adams

BodyBeat: a mobile system for sensing non-speech body sounds

In this paper, we propose BodyBeat, a novel mobile sensing system for capturing and recognizing a diverse range of non-speech body sounds in real-life scenarios. Non-speech body sounds, such as sounds of food intake, breath, laughter, and cough contain invaluable information about our dietary behavior, respiratory physiology, and affect. The BodyBeat mobile sensing system consists of a custom-built piezoelectric microphone and a distributed computational framework that utilizes an ARM microcontroller and an Android smartphone. The custom-built microphone is designed to capture subtle body vibrations directly from the body surface without being perturbed by external sounds. The microphone is attached to a 3D printed neckpiece with a suspension mechanism. The ARM embedded system and the Android smartphone process the acoustic signal from the microphone and identify non-speech body sounds. We have extensively evaluated the BodyBeat mobile sensing system. Our results show that BodyBeat outperforms other existing solutions in capturing and recognizing different types of important non-speech body sounds.

DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar

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 users 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.

Mindless computing: designing technologies to subtly influence behavior

Persuasive technologies aim to influence users behaviors. In order to be effective, many of the persuasive technologies developed so far relies on users motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on users motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.

EmotionCheck: leveraging bodily signals and false feedback to regulate our emotions

In this paper we demonstrate that it is possible to help individuals regulate their emotions with mobile interventions that leverage the way we naturally react to our bodily signals. Previous studies demonstrate that the awareness of our bodily signals, such as our heart rate, directly influences the way we feel. By leveraging these findings we designed a wearable device to regulate users anxiety by providing a false feedback of a slow heart rate. The results of an experiment with 67 participants show that the device kept the anxiety of the individuals in low levels when compared to the control group and the other conditions. We discuss the implications of our findings and present some promising directions for designing and developing this type of intervention for emotion regulation.

Public restroom detection on mobile phone via active probing

Although there are clear benefits to automatic image capture services by wearable devices, image capture sometimes happens in sensitive spaces where camera use is not appropriate. In this paper, we tackle this problem by focusing on detecting when the user of a wearable device is located in a specific type of private space---the public restroom---so that the image capture can be disabled. We present an infrastructure-independent method that uses just the microphone and the speaker on a commodity mobile phone. Our method actively probes the environment by playing a 0.1 seconds sine wave sweep sound and then analyzes the impulse response (IR) by extracting MFCCs features. These features are then used to train an SVM model. Our evaluation results show that we can train a general restroom model which is able to recognize new restrooms. We demonstrate that this approach works on different phone hardware. Furthermore, the volume levels, occupancy and presence of other sounds do not affect recognition in significant ways. We discuss three types of errors that the prediction model has and evaluate two proposed smoothing algorithms for improving recognition.

Nutrilyzer: A Mobile System for Characterizing Liquid Food with Photoacoustic Effect

In this paper, we propose Nutrilyzer, a novel mobile sensing system for characterizing the nutrients and detecting adulterants in liquid food with the photoacoustic effect. By listening to the sound of the intensity modulated light or electromagnetic wave with different wavelengths, our mobile photoacoustic sensing system captures unique spectra produced by the transmitted and scattered light while passing through various liquid food. As different liquid foods with different chemical compositions yield uniquely different spectral signatures, Nutrilyzers signal processing and machine learning algorithm learn to map the photoacoustic signature to various liquid food characteristics including nutrients and adulterants. We evaluated Nutrilyzer for milk nutrient prediction (i.e., milk protein) and milk adulterant detection. We have also explored Nutrilyzer for alcohol concentration prediction. The Nutrilyzer mobile system consists of an array of 16 LEDs in ultraviolet, visible and near-infrared region, two piezoelectric sensors and an ARM microcontroller unit, which are designed and fabricated in a printed circuit board and a 3D printed photoacoustic housing.

BodyBeat: Eavesdropping on our Body Using a Wearable Microphone

14 [highLights] f rom munching on a piece of toast and swallowing a sip of coffee to deep breathing after a few laps of running, our body continually makes a wide range of non-speech body sounds, which can be indicative of our dietary behaviour, respiratory physiology, and affect. A wearable system that can continuously capture and recognize different types of body sound with high fidelity can also be used for behavioural tracking and disease diagnosis. BodyBeat is such a mobile sensing system that can detect a diverse range of non-speech body sounds in real-life scenarios. The BodyBeat mobile sensing system consists of a custom-built piezoelectric microphone and a distributed computational framework that utilizes an ARM microcontroller and an Android smartphone. The custom-built microphone is designed to capture subtle body vibrations directly from the body surface without being disturbed by external sounds. The ARM embedded system and the Android smartphone processes the acoustic signal from the microphone and identifies non-speech body sounds. Speech is not the only sound generated by human. Non-speech body sounds such as sounds of food intake, breath, laughter, yawn, and cough contain invaluable information about peoples health and wellbeing. With regard to food intake, body sounds enable us to discriminate characteristics of food and drinks [1, 2]. Longer term tracking of eating sounds could be very useful in dietary monitoring applications. Breathing sounds, generated by the friction caused by the airflow from our lungs through the vocal organs (e.g., trachea, larynx, etc.) to the mouth or nasal cavity [3], are highly indicative of the conditions of our lungs. Sounds of laughter and yawns are good indicators of peoples affect states such as happiness and fatigue. Therefore, automatically tracking these non-speech body sounds can help in early detection of negative health indicators by performing regular dietary monitoring, pulmonary function testing, and affect sensing. We have designed, implemented, and evaluated a mobile sensing system called BodyBeat, which could continuously keep tracking of a diverse set of non-speech body sounds. BodyBeat consists of a custom-made piezoelectric sensor-based microphone, an ARM microcontroller, and

Real time heart rate and breathing detection using commercial motion sensors

In this demo, we present a contactless breathing and heart rate sensing system that continuously and unobtrusively tracks physiological signals using commercial off-the-shelf radar modules. Our system provides a single sensor solution to track physical and physiological variables including coarse body movements as well as subtle and fine-grained chest movements due to breathing and heartbeat. Continuous tracking of these physiological variables especially, throughout the night can be used for sleep stage mining.

Survey of audio programming tools

Audio programming can be an overwhelming and confusing task that many developers are not adequately prepared for. Even the seemingly simple task of choosing the right software developers kit (SDK) to use can become a difficult task. This paper presents an analysis of the most extensive and widely used audio programming SDKs organized by audio task and highlighting factors such as usability, support, and functionality.

Keppi: A Tangible User Interface for Self-Reporting Pain

Motivated by the need to support those managing chronic pain, we report on the iterative design, development, and evaluation of Keppi, a novel pressure-based tangible user interface (TUI) for the self-report of pain intensity. In-lab studies with 28 participants found individuals were able to use Keppi to reliably report low, medium, and high pain as well as map squeeze pressure to pain level. Based on insights from these evaluations, we ultimately created a wearable version of Keppi with multiple form factors, including a necklace, bracelet, and keychain. Interviews indicated high receptivity to the wearable design, which satisfied additional user-identified needs (e.g., discreet and convenient) and highlighted key directions for the continued refinement of tangible devices for pain assessment.