An Open Source Mobile Platform for Psychophysiological Self Tracking Andrea GAGGIOLIa,1, Pietro CIPRESSOa, Silvia SERINOa, Giovanni PIOGGIA b, Gennaro TARTARISCO b, Giovanni BALDUS b, Daniele CORDA b and Giuseppe RIVAa a Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano b National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy
Abstract. Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from preprogrammed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone’s memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/). Keywords. Computerized experience sampling, ECG, wearable sensors, selftracking, smartphones
Introduction Self-tracking is a fast-growing trend in the field of e-health that consists in the collection of medical data including symptoms, biomarkers, responses to previous treatments [1]. This approach is enabled by the growing convergence between ubiquitous computing and wearable microsensors, which allow personal health data to be collected, aggregated, visualized, collated into reports and shared. Insights gained from these measurements can be used, for example, to change life-threatening habits, adopt healthier lifestyle, or take more informed treatment decisions [2]. In the field of psychology, however, self-tracking is not a novel concept. This approach was developed by Csikszentmihalyi and Larson in 1983, much before the advent of personal informatics [3]. They created a paper-and-pencil methodology, called Experience Sampling Method (ESM), which requires participants to fill out multiple brief questionnaires about their current activities and feelings by responding to random alerts throughout the day. Since then, ESM has been used widely with adolescent and 1
Corresponding Author: Andrea Gaggioli, Istituto Auxologico Italiano, Milan, Italy; Email:
[email protected]
adult populations to investigate areas such as mood, social interactions and time use. In the past, ESM-based studies have been mainly done via paper and pencil measures. However, in the last decade computerized versions of this technique have been introduced, which allows collecting data by handheld electronic devices [4]. Here, we describe the key functionalities of PsychLog, a mobile experience sampling application that allows collect users’ psychological, physiological (ECG), and activity data for mental health research.
1. Description of the System PsychLog (http://sourceforge.net/projects/psychlog/) is a mobile experience sampling platform that allows the collection of psychological, physiological and activity information in naturalistic settings. Specifically, the application allows administering self-report questionnaires to gather participants' feedback on his/her quality of experience in its various cognitive, affective and motivational dimensions. The system consists of three main modules: the survey manager module, the sensing/computing module and the visualization module. The survey manager module allows configuring, managing and administering self-report questionnaires. Surveys are used to collect participants' feedback on his/her quality of experience in its various cognitive, affective and motivational dimensions. The researcher defines the schedule of self-reports by setting a trigger. Triggers can be launched with a fixed schedule or randomly during a day. The sensing/computing module allows continuously monitoring hearth rate and activity data acquired from a wireless electrocardiogram (ECG) equipped with a threeaxis accelerometer. The wearable sensor platform includes a board that allows the transduction, amplification and pre-processing of raw sensor signals, and a Bluetooth transmitter to wirelessly send the processed data. Sensed data are transmitted to the mobile phone Bluetooth receiver and gathered by the PsychLog computing module, which stores and process the signals for the extraction of relevant features. ECG and accelerometer sampling intervals (epochs) can be fully tailored to the study’s design. During each epoch, signals are sampled at 250 Hz, filtered to eliminate common noise sources using Notch filter at 0 Hz and low pass at 35 Hz and analogue-to-digital converted with 12-bit accuracy in the ±3 V range. The PsychLog application extracts QRS peaks through a dedicated algorithm [5] and R-R interval time series. The movement information is the variance of the magnitude of the three-axis acceleration vector. Finally, the visualization module allows plotting in real time ECG and acceleration graphs on the mobile phone’s screen. This feature is useful either for monitoring the ECG data or for checking the functioning of the ECG sensor apparatus. Self-reports and sensors data are stored on the mobile phone’s internal memory, in separate files, for off-line analysis.
2. Pilot Deployment The usability and functionality of the PsychLog systems were tested in a pilot trial that involved eight participants (four males and four females, mean age: 22). All participants volunteered to take part in the study and signed an informed consent. The testing procedure required participants carrying the smartphone and ECG sensor with them for one week and responding to self-reports. The application was configured to
launch the surveys at random times during waking hours. The ESM form asked participants questions related to the activity carried out at the moment of the beep, context of activity, content of thoughts and other items investigating cognitive, affective and motivational dimensions of experience. The ECG sampling epoch was set to 20 minutes. At the end of the week, participants returned the smartphone and sensor to the lab and were interviewed in order to get their feedback on the overall acceptability of the system and of the procedure. All interviews were recorded and typewritten. Out of 291 beeps, participants filled 272 valid reports (93%) and 275 ECG sampling were recorded (94%). Findings showed that all participants found the application very easy to learn and did not report specific difficulties in filling the ESM questionnaires. To evaluate ECG signal quality, we measured the number of detectable artifacts in the raw ECG signal. A total of 220 ECG plots were included in the analysis. Of them, 13 ECG plots were completely artifacts-free; 154 had up to three artifacts; 53 ECG had between four and eight artifacts; none had more than eight artifacts. These findings suggest that although the ECG was recorded in natural settings and participants were free to move, the ECG signal included very few artifacts as compared to standard ECG detection based on literature. Actually, these artifacts can be easily removed using standard artifacts correction algorithms.
3. Conclusions and Future Steps In this paper, we have described a mobile data collection platform for psychophysiological research. The pilot evaluation results showed good level of acceptance by participants and the analysis of raw ECG signals detected a small number of artifacts. A still open issue concerns the impact of physiological signal acquisition on CPU and battery consumption. The performance test found that the battery of the smartphone is drained in about 5 hours under continuous wireless sampling. Therefore, prolongation and optimization of battery life is considered an important goal for future implementations.
Acknowledgments This work was supported by the European-funded project "Interstress-Interreality in the management and treatment of stress-related disorders”, FP7-247685.
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