Name
Abstract | ||
|---|---|---|
Healthcare applications supported by the Internet of Things enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery-powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, particularly energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this article, we propose two approaches: myopic and Markov decision processes (MDPs)—to consider both energy constraints and risk factor requirements for achieving a twofold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient’s vital signs. Vital signs, including heart rate, respiration rate, and oxygen saturation, are extracted from a photoplethysmogram signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor’s sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable photoplethysmogram sensor in an IoT system, and show that compared to nonadaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality misdetection on the order of 0.17 in a 24-hour health monitoring system. In addition, over 4 weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection compared to the myopic method. We illustrate results comparing myopic, MDP, and nonadaptive methods to monitor 14 subjects over 1 month.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3351286 | ACM Transactions on Computing for Healthcare |
Keywords | DocType | Volume |
Health monitoring,Internet of Things,abnormality detection,context awareness,edge-assisted control,edge/fog computing,energy efficiency,wearable electronics | Journal | 1 |
Issue | ISSN | Citations |
2 | 2637-8051 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Delaram Amiri | 1 | 0 | 0.34 |
| Arman Anzanpour | 2 | 38 | 5.93 |
| Iman Azimi | 3 | 3 | 2.44 |
| Marco Levorato | 4 | 464 | 49.51 |
| Pasi Liljeberg | 5 | 1147 | 92.79 |
| Nikil Dutt | 6 | 4960 | 421.49 |
| Amir M. Rahmani | 7 | 5 | 3.45 |