Episodic sampling: towards energy-efficient patient monitoring with wearable sensors

Adaptive Data Transmission Algorithm for the System of Inertial Sensors for Hand Movement Acquisition

Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the teletransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (p > 0.05) between the signal reconstructed from the complete data and processed by the algorithm.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

A General Framework for Making Context-Recognition Systems More Energy Efficient

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

Self-Powered Multiparameter Health Sensor

Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit-for evaluation of biomedical sensors and monitoring applications (http://ulpsek.com). ULPSEK includes a multiparameter sensor measuring and processing electrocardiogram, respiration, motion, body temperature, and photoplethysmography. Instead of a battery, ULPSEK is powered using an efficient body heat harvester. The harvester produced 171 W on average, which was sufficient to power the sensor below 25 C ambient temperature. We present design issues regarding the power supply and the power distribution network of the ULPSEK sensor platform. Due to the security aspect of self-powered health sensors, we suggest a hybrid solution consisting of a battery charged by a harvester.

Validation of an acoustic gastrointestinal surveillance biosensor for postoperative ileus

BACKGROUND

Postoperative ileus (POI) can worsen outcomes, increase cost, and prolong hospitalization. An objective marker could help identify POI patients who should not be prematurely fed. We developed a disposable, non-invasive acoustic gastro-intestinal surveillance (AGIS) biosensor. We tested whether AGIS can distinguish healthy controls from patients recovering from abdominal surgery.

STUDY DESIGN

AGIS is a disposable plastic device embedded with a microphone that adheres to the abdominal wall and connects to a computer that measures acoustic event rates. We compared intestinal rates of healthy subjects using AGIS for 60 min after a standardized meal to recordings of two postoperative groups: (1) patients tolerating standardized feeding and (2) POI patients. We compared intestinal rates among groups using ANOVA and t tests.

RESULTS

There were 8 healthy controls, 7 patients tolerating feeding, and 25 with POI; mean intestinal rates were 0.14, 0.03, and 0.016 events per second, respectively (ANOVA p < 0.001). AGIS separated patients from controls with 100 % sensitivity and 97 % specificity. Among patients, rates were higher in fed versus POI subjects (p = 0.017).

CONCLUSION

Non-invasive, abdominal acoustic monitoring distinguishes POI from non-POI subjects. Future research will test whether AGIS can identify patients at risk for development of POI and assist with postoperative feeding decisions.

CARER: efficient dynamic sensing for continuous activity monitoring

Advancement in wireless health sensor systems has triggered rapidly expanding research in continuous activity monitoring for chronic disease management or promotion and assessment of physical rehabilitation. Wireless motion sensing is increasingly important in treatments where remote collection of sensor measurements can provide an in-field objective evaluation of physical activity patterns. The well-known challenge of limited operating lifetime of energy-constrained wireless health sensor systems continues to present a primary limitation for these applications. This paper introduces CARER, a software system that supports a novel algorithm that exploits knowledge of context and dynamically schedules sensor measurement episodes within an energy consumption budget while ensuring classification accuracy. The sensor selection algorithm in the CARER system is based on Partially Observable Markov Decision Process (POMDP). The parameters for the POMDP algorithm can be obtained through standard maximum likelihood estimation. Sensor data are also collected from multiple locations of the subjects body, providing estimation of an individual's daily activity patterns.

Energy-efficient context classification with dynamic sensor control

Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.