CRNTC+ : A smartphone-based sensor processing framework for prototyping personal healthcare applications

While smartphone apps for health monitoring and patient support are of great interest to care providers and patients alike, suitable development and evaluation frameworks are currently lacking. We present and evaluate an Android open-source smartphone framework CRNTC+ for sensors data acquisition, signal processing, pattern analysis, interaction and feedback, based on the Context Recognition Network Toolbox (CRNT). CRNTC+ extends the original CRNT by providing components to read smartphone and external sensor data, supporting annotations, and various output components. Here, we formally evaluate CRNTC+ regarding extensibility, scalability, and energy consumption. We present study results where CRNTC+ was deployed in an application to detect epileptic seizures. Results showed that CRNTC+ is well-suited for prototyping health applications in real-life, where online sensor data recording and recognition is needed

CRNTC+ : A smartphone-based sensor processing framework for prototyping personal healthcare applications

While smartphone apps for health monitoring and patient support are of great interest to care providers and patients alike, suitable development and evaluation frameworks are currently lacking. We present and evaluate an Android open-source smartphone framework CRNTC+ for sensors data acquisition, signal processing, pattern analysis, interaction and feedback, based on the Context Recognition Network Toolbox (CRNT). CRNTC+ extends the original CRNT by providing components to read smartphone and external sensor data, supporting annotations, and various output components. Here, we formally evaluate CRNTC+ regarding extensibility, scalability, and energy consumption. We present study results where CRNTC+ was deployed in an application to detect epileptic seizures. Results showed that CRNTC+ is well-suited for prototyping health applications in real-life, where online sensor data recording and recognition is needed

Methods for monitoring the human circadian rhythm in free-living

Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, we estimate light exposure from smartphone sensor and weather in- formation. We applied the Kronauer model to compare the phase shift resulting from head light measurements, wrist measurements, and smartphone estimations. We found it was possible to monitor circadian phase shift from light estimation based on smartphone sensor and weather information with a weekly error of 32±17min, which outperformed wrist measurements in 11 out of 12 participants. Sleep could be detected from smartphone use with an onset error of 40±48 min and wake error of 42±57 min. Screen use could be detected smart eyeglasses with 0.9 ROC AUC for ambient light intensities below 200lux. Nine clusters of ADLs were distinguished using Gaussian mixture models with an average accuracy of 77%. In conclusion, a combination of the proposed smartphones and smart eyeglasses applications could support users in synchronising their circadian clock to the external clocks, thus living a healthier lifestyle