mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions

被引:37
|
作者
Hossain, Syed Monowar [1 ]
Hnat, Timothy [1 ]
Saleheen, Nazir [1 ]
Nasrin, Nusrat Jahan [1 ]
Noor, Joseph [2 ]
Ho, Bo-Jhang [2 ]
Condie, Tyson [2 ]
Srivastava, Mani [2 ]
Kumar, Santosh [1 ]
机构
[1] Univ Memphis, Memphis, TN 38152 USA
[2] Univ Calif Los Angeles, Los Angeles, CA 90024 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
mHealth; mobile sensor big data; software architecture;
D O I
10.1145/3131672.3131694
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 real-time 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.
引用
收藏
页数:14
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