An official website of the United States government
Home-based health monitoring systems are important to many conditions (e.g., aging, chronic diseases). The absence of suitable data collection infrastructure is a fundamental barrier to the development of related algorithms and systems. In this poster, we present Proteus, a robust, extensible and scalable data collection infrastructure, to enable small research teams to manage large deployments. We identify the desired features and achieve them by combining mature technologies and new components: i) extensibility with new, diverse sensor types and data formats with a few lines of coding (LOC) efforts; ii) scalability in managing sensor/edge devices to automate many deployment, management tasks; iii) resilience to system failures and network outage. Experiments on a prototype show zero data loss or system error for one sensor node running 10 days, and 99.95% of data received for 32 emulated sensors sending data at 200 Mbps, 20 and 100 fold reductions in node setup efforts and LOC for new sensor types. The preliminary results show Proteus is promising for large-scale longitudinal deployment of home-based health monitoring.
more »
« less
Proteus: Towards a Manageability-focused Home-based Health Monitoring Infrastructure
A data collection infrastructure is vital for generating sufficient amounts and diversity of data necessary for developing algorithms in home-based health monitoring. However, the manageability— deployment and operation efforts—of such an infrastructure has long been overlooked. Even a small size of a dozen homes may incur enormous manual efforts on the research team, including installing, configuring and updating of sensor, edge devices; continuous monitoring for faults and errors to prevent data losses, and integrating new sensing modalities. In this paper, we present Proteus, an easily managed infrastructure designed to automate much of the work in deploying and operating such systems. Proteus includes: i) scalable, continuous deployment and update of devices with automatic bootstrapping; ii) automatic fault and error monitoring and recovery with watchdogs and LED feedback, and complementary edge and cloud storage backups; and iii) an easy-to-use data-agnostic pipeline for integrating new modalities. We demonstrate our system’s robustness through different sets of experiments: 3 sensor nodes running for 24 days sending data (17.3 Mbps aggregate rate), and 16 emulated sensors (92.8 Mbps aggregate rate). All such experiments have data loss rates less than 1%. Further we reduce human efforts by 25-fold and code required for adding new data modality by 25-fold. Our results show that Proteus is a promising solution for enabling research teams to effectively manage home-based health monitoring at small to medium sizes.
more »
« less
- Award ID(s):
- 1951880
- PAR ID:
- 10536400
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701269
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Houston TX USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Home networks lack the powerful security tools and trained personnel available in enterprise networks. This compli- cates efforts to address security risks in residential settings. While prior efforts explore outsourcing network traffic to cloud or cloudlet services, such an approach exposes that network traffic to a third party, which introduces privacy risks, particularly where traffic is decrypted (e.g., using Transport Layer Security Inspection (TLSI)). To enable security screening locally, home networks could introduce new physical hardware, but the capital and deployment costs may impede deployment. In this work, we explore a system to leverage existing available devices, such as smartphones, tablets and laptops, already inside a home network to create a platform for traffic inspection. This software-based solution avoids new hardware deployment and allows decryption of traffic without risk of new third parties. Our investigation compares on-router inspection of traffic with an approach using that same router to direct traffic through smartphones in the local network. Our performance evaluation shows that smartphone middleboxes can substantially increase the throughput of communication from around 10 Mbps in the on-router case to around 90 Mbps when smartphones are used. This approach increases CPU usage at the router by around 15%, with a 20% CPU usage increase on a smartphone (with single core processing). The network packet latency increases by about 120 milliseconds.more » « less
-
Abstract 24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We presentSCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluateSCALINGvia simulations and two testbeds (in lab and home configurations of sizes 3$$\times$$ 6 sq m and 4.5$$\times$$ 8.5 sq m). Experimental results demonstrate thatSCALINGoutperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed withSCALINGcompared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios.more » « less
-
The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge this gap, we present mRI1, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k synchronized frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality. We hope that the release of mRI can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly facilitate the applications of home-based health monitoring.more » « less
-
24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly re- searchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily setup by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3 × 6 sq m and 4.5 × 8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking,respectively.Notably,only1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3584371.3613006
