# From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques arXiv: https://arxiv.org/abs/2107.00948 ยท HTML: https://mlsanity.com/paper/2107.00948/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 407 references that are in the map. - First posted on arXiv 2 Jul 2021. ## Builds on (most cited first, 24 of 24) - [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019) - [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020) - [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020) - [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020) - [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015) - [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020) - [Self-supervised transfer learning of physiological representations from free-living wearable data](https://mlsanity.com/paper/2011.12121/) (2020) - [Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data](https://mlsanity.com/paper/2102.05225/) (2021) - [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021) - [An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions](https://mlsanity.com/paper/1706.09090/) (2017) - [NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions](https://mlsanity.com/paper/1911.07179/) (2019) - [Impact of Physical Activity on Sleep:A Deep Learning Based Exploration](https://mlsanity.com/paper/1607.07034/) (2016) - [Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs](https://mlsanity.com/paper/2003.11511/) (2020) - [Using mobile phone sensor technology for mental health research: Integrated analysis to identify hidden challenges and potential solutions](https://mlsanity.com/paper/1805.09158/) (2018) - [Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis](https://mlsanity.com/paper/1908.06403/) (2019) - [BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond](https://mlsanity.com/paper/2005.10103/) (2020) - [Quality of Information in Mobile Crowdsensing: Survey and Research Challenges](https://mlsanity.com/paper/1709.03583/) (2017) - [WeTrace -- A Privacy-preserving Mobile COVID-19 Tracing Approach and Application](https://mlsanity.com/paper/2004.08812/) (2020) - [Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing](https://mlsanity.com/paper/2011.11694/) (2020) - [Data Driven Authentication: On the Effectiveness of User Behaviour Modelling with Mobile Device Sensors](https://mlsanity.com/paper/1410.7743/) (2014) - [A Large-scale Concurrent Data Anonymous Batch Verification Scheme for Mobile Healthcare Crowd Sensing](https://mlsanity.com/paper/1804.01822/) (2018) - [How to Return to Normalcy: Fast and Comprehensive Contact Tracing of COVID-19 through Proximity Sensing Using Mobile Devices](https://mlsanity.com/paper/2004.12576/) (2020) - [An Investigation of Containment Measures Against the COVID-19 Pandemic in Mainland China](https://mlsanity.com/paper/2007.08254/) (2020) - [Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health](https://mlsanity.com/paper/2012.11646/) (2020) ## Built on by (most cited first, 7 of 7) - [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022) - [AudioInsight: Detecting Social Contexts Relevant to Social Anxiety from Speech](https://mlsanity.com/paper/2407.14458/) (2024) - [Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline](https://mlsanity.com/paper/2304.09928/) (2023) - [Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach](https://mlsanity.com/paper/2402.06654/) (2024) - [Visual Analytics Using Tensor Unified Linear Comparative Analysis](https://mlsanity.com/paper/2507.19988/) (2025) - [Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing](https://mlsanity.com/paper/2312.12666/) (2023) - [An AI-driven framework for the prediction of personalised health response to air pollution](https://mlsanity.com/paper/2505.10556/) (2025) ## Abstract (arXiv) Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either (a) personalized medicine for individuals or (b) public health for populations, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- (i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: (1) Sensing Task Creation & Participation, (2) Health Surveillance & Data Collection, and (3) Data Analysis & Knowledge Discovery. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.