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Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.

The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage. CCS Concepts • Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; • Computer systems organization → Embedded and cyber-physical systems; ACM Reference format Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In Proceedings of SenSys ′17, Delft, Netherlands, November 6–8, 2017, 14 pages.

A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.