Edward Sazonov

University of Alabama - Tuscaloosa, Electrical and Computer Engineering, Faculty Member

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Edward Sazonov (IEEE M’02, SM’11) received the Diploma of Systems Engineer from Khabarovsk State University of Technology, Russia, in 1993 and the Ph.D. degree in Computer Engineering from West Virginia University, Morgantown, WV, in 2002. Currently he is a Professor in the Department of Electrical and Computer Engineering at the University of Alabama, Tuscaloosa, AL and the head of the Computer Laboratory of Ambient and Wearable Systems (http://claws.eng.ua.edu). His research interests span wearable devices, sensor-based behavioral informatics and methods of biomedical signal processing and pattern recognition. Devices developed in his laboratory include a wearable sensor for objective detection and characterization of food intake (AIM – Automatic Ingestion Monitor)

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Guest Editorial Special Issue on Self-Powered Sensors: Architectures, Applications, and Challenges

IEEE Sensors Journal, Sep 14, 2023

A T present, the sustainability of a sensor becomes a hindrance due to energy storage's limited l... more A T present, the sustainability of a sensor becomes a hindrance due to energy storage's limited lifetime. In this case, self-powered sensors are proposed to solve this issue. While self-powered sensors are promising, there are still some challenges to overcome before they can be deployed on a large scale. Therefore, this Special Issue aims to solicit original papers with discussions on self-powered sensors, as well as their architectures, applications, and challenges. Therefore, this Special Issue has provided a forum for the research community to share advances and new ideas in selfpowered sensors. Based on the reviewers' feedback, as well as the evaluations of editors, some papers are selected in this Special Issue from 190 submissions. We are very thankful to all authors who have contributed to this Special Issue. A variety of topics are proposed and discussed in this Special Issue through its accepted papers. Due to the limitation of the editorial space, only three papers will be introduced here. In [A1], Wright et al. stated that self-powered sensors are expected to enable new large-scale deployment and location access capabilities for sensor systems. Energy harvesting from power line infrastructure offers an architecture for addressing some challenges in self-powered sensors. In this article, an inductive power line harvester concept is presented, based on a flux concentration approach adapted to a closed-loop core geometry. The experimental results show this performance is adequate for enabling self-powered wireless sensor networks installed along power distribution lines. The article by Javaid et al. [A2] described the current literature lacks a comprehensive review on state-of-the-art design solutions for self-powered sensors and their implementation challenges. To address the limitations, the authors have reviewed state-of-the-art architectures of self-powered sensors and their applications in various domains. Moreover, the authors discuss the implementation challenges of selfpowered sensors that affect their long-term operations, and some possible solutions. In [A3], Qiu et al. stated that self-powered sensors solve the most shackling issue in the current sensing field, thus enabling many previously unattainable application scenarios. In this paper, a variable-length continuum manipulator with self-powered sensors is designed, and an adaptive capture control scheme based on deep-learning prediction is proposed for the envelope grasp of a fixed cylinder target and the hook grasp of a ring target moving in a small range. The experimental results on a prototype reveal that the generated

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OR09-07-23 Dietary Assessment Protocol in the Nutrition for Precision Health (NPH) Study

Current Developments in Nutrition

Ear canal pressure sensor for food intake detection

Frontiers in Electronics

Introduction: This paper presents a novel Ear Canal Pressure Sensor (ECPS) for objective detectio... more Introduction: This paper presents a novel Ear Canal Pressure Sensor (ECPS) for objective detection of food intake, chew counting, and food image capture in both controlled and free-living conditions. The contribution of this study is threefold: 1) Development and validation of a novel wearable sensor that uses changes in ear canal pressure and the device’s acceleration as an indicator of food intake, 2) A method to identify chewing segments and count the number of chews in each eating episode, and 3) Facilitation of egocentric image capture only during eating by triggering camera from sensor detection thus reducing power consumption, privacy concerns, as well as storage and computational cost.Methods: To validate the device, data were collected from 10 volunteers in a controlled environment and three volunteers in a free-living environment. During the controlled activities, each participant wore the device for approximately 1 h, and during the free living for approximately 12 h. The...

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The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device

Frontiers in Nutrition

IntroductionThe aim of this feasibility and proof-of-concept study was to examine the use of a no... more IntroductionThe aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options.MethodsData from 25 participants and 116 total days (7 men, 18 women, Mage = 44 ± 12 years, BMI 34.3 ± 5.2 kg/mm2), who wore the passive capture device for at least 7 consecutive days (≥12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack.ResultsThe most prevalent e...

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Implementing Real-Time Food Intake Detection in a Wearable System Using Accelerometer

2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)

The objective of this study was to develop a realtime food intake detection method in a wearable ... more The objective of this study was to develop a realtime food intake detection method in a wearable sensor system using an accelerometer sensor. The Automatic Ingestion Monitor v2 (AIM-2) wearable sensor consists of a chewing sensor, accelerometer, camera, microcontroller, battery, and other components. The accelerometer is used to capture signals of chewing and head-movements. Time-domain statistical features were computed from the 3-axis accelerometer signal. A decision tree classifier was trained offline and implemented in the microcontroller to predict food intake events in real-time. The classifier was trained on a dataset collected from 30 free-living volunteers. The classifier achieved a sensitivity of 86.79%, specificity of 96.24%, and F1-score of 57.57% in leave-one-out subject validation. The proposed method was implemented and tested for eight days of free-living data and achieved 97.62% accuracy of eating episode detection. Real-time food intake detection in a wearable sensor system may enable future implementation of just-in-time feedback to the user for improving eating habits and achieving weight-loss.

A Comparative Study of Deep Learning Algorithms for Detecting Food Intake

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Analyzing Impact of Mouthpiece-based Puff Topography Devices on Smoking Behavior using Wearable Sensors

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Design and Preliminary Testing of an Instrumented Exoskeleton for Walking Gait Measurement

2019 SoutheastCon

This paper presents the design and preliminary testing of an instrumented exoskeleton system, whi... more This paper presents the design and preliminary testing of an instrumented exoskeleton system, which is targeted at collecting gait data of the human locomotion to support the controller development of lower-limb wearable robots. This compact and lightweight device features a unique two-degree-of-freedom joint to minimize the interference to the user movement and a simple yet effective adjustment mechanism to fit subjects at different heights. For the gait measurement, the device incorporates embedded joint goniometers to obtain the knee and ankle positions, and inertial measurement units to obtain three-dimensional kinematic information. Force-sensing resistors are also incorporated into the shoe insole for plantar pressure measurement. Sensor signals are routed to an onboard microcontroller system for data storage and transfer, and the system is fully self-contained with onboard battery to facilitate data collection in various environments. A prototype of the exoskeleton was fabricated, and preliminary testing was conducted on two healthy subjects in various postures and modes of movement (walking, sitting, standing, stair climbing, etc.). The evaluation of a temporal event detection test showed no more than 5.5% mean variation in the measure of step counts by the sensory system and video annotation. These results indicate that the exoskeleton can provide an accurate measurement of gait information, using measurements taken from external video recordings as the benchmark in this preliminary validation study.

Development of a Smart IoT Charger for Wearable Cigarette Smoking Monitor

2019 SoutheastCon

Wearable sensors have been proven effective for the objective detection of smoking events and ind... more Wearable sensors have been proven effective for the objective detection of smoking events and individual puffs. For a precise analysis of the smoking habit of a person, these sensing methods need to be thoroughly applied for weeks or months. However, the limitation of sensor battery longevity, concern for sensors’ timestamp drift or parameter calibration, and the restricted sensor data evaluation process limit this long-term application and force subjects, participating in the smoking study, for frequent returns to lab facilities. Also, sensor data analysis reports such as the statistics of daily cigarette consumption are not instantly available to smokers. Addressing these limitations, a smart IoT charger based on a Raspberry Pi (RPi) is presented in this paper, which can serve to a) recharge the sensor battery b) update the sensor timestamp c) exchange control commands d) extract data from the sensor system and, e) instant upload of sensor data to a cloud server to store and process sensor signals. To validate the proposed system, two volunteer smokers applied the wrist inertial sensor of a wearable sensor system for five consecutive days and used the proposed RPi charger daily. The difference in charging current provided by the smart charger and the commercial wall charger was found to be less than 0.1%. For sensor files of ∼100KB size, the average file transfer rates and upload rates were found to be 3.25 MBps and 1.04 MBps, respectively, which was satisfactory for the current application. This smart charger approach is also envisioned to become a personalized tool to assist clinical smoking intervention methods by providing a method to learn daily consumption statistics.

Development of Cloud-based Infrastructure for Real Time Analysis of Wearable Sensor Signal

2022 IEEE World AI IoT Congress (AIIoT)

Cigarette Smoke Exposure Computation using Bioimpedance Sensor

2020 IEEE SENSORS

Understanding the smoking habit and the amount of smoke exposure of a smoker is crucial for smoki... more Understanding the smoking habit and the amount of smoke exposure of a smoker is crucial for smoking intervention. The parameters extracted from breathing patterns, such as puff duration, inhale-exhale duration, etc. can play an important role in defining the amount of smoke exposure. This paper is the first to assess the suitability of a bio-impedance sensor for measurement of the smoke exposure metrics from breathing. The Personal Automatic Cigarette Tracker version 2.0 (PACT 2.0) was used in this study. The bio-impedance sensor was placed at the thoracic level with the help of an instrumented T-shirt. The bio-impedance signal during the smoking event was observed and the Respiratory Smoke Exposure Measurement (RSEM) algorithm was used to compute the smoke exposure metrics. The metrics computed by the RSEM algorithm were compared statistically with gold standard obtained from video annotations. The paired t-test for puff duration and inhale-exhale duration obtained from bio-impedance signal and video annotations indicated that the values were statistically similar. The results of this study indicate the potential use of bio-impedance sensor for measurement of smoke exposure metrics.

Reproducibility of Dietary Intake Measurement From Diet Diaries, Photographic Food Records, and a Novel Sensor Method

Frontiers in Nutrition, 2020

Objective: No data currently exist on the reproducibility of photographic food records compared t... more Objective: No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake. Method: This was a retrospective analysis of data from a previous study, in which 30 participants (15 female), aged 29 ± 12 y and having a BMI of 27.9 ± 5.5, consumed three identical meals on different days. Four different methods were used to estimate total mass and energy intake on each day: (1) weighed food record; (2) photographic food record; (3) diet diary; and (4) novel mathematical model based on counts of chews and swallows (CCS models) obtained via the use of electronic sensors and video monitoring system. The study staff conducted weighed food records for all meals, took pre-and post-meal photographs, and ensured that diet diaries were completed by participants at the end of each meal. All methods were compared against the weighed food record, which was used as the reference method. Results: Reproducibility was significantly different between the diet diary and photographic food record for total energy intake (p = 0.004). The photographic record had greater reproducibility vs. the diet diary for all parameters measured. For total energy intake, the novel sensor method exhibited good reproducibility (repeatability coefficient (RC) of 59.9 (45.9, 70.4), which was better than that for the diet diary [RC = 79.6 (55.5, 103.3)] but not as repeatable as the photographic method [RC = 43.4 (32.1, 53.9)]. Photographic food records offer superior precision to the diet diary and, therefore, would be valuable for longitudinal studies with repeated measures of dietary intake. A novel electronic sensor also shows promise for the collection of longitudinal dietary intake data.

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A systematic review of wearable technology and wearable biosensor usage in aging population (Preprint)

BACKGROUND Background: The emergence of the Internet of Things (IoT) has subsequently catalysed t... more BACKGROUND Background: The emergence of the Internet of Things (IoT) has subsequently catalysed the advent of wearable technology. Given the inevitability of ageing worldwide, wearable technology should evolve in terms of feasibility within the ageing population. However, there is limited systematic review on wearable technology in aging population. OBJECTIVE Objective: This systematic review aims to offer comprehensive current state and information on wearable technology research among older adults. METHODS Methods: This review implements Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. 38 of the 618 publications were screened based on titles and abstracts following the inclusion and exclusion criteria through seven databases (China National Knowledge Infrastructure, Chinese Science Citation Database, Cochrane, IEEE Xplore, PubMed, Scopus, and WOS) and two search engines (Google Scholar and Microsoft Academic). RESULTS Results: Most of the studies included in t...

Detection of Food Intake Sensor’s Wear Compliance in Free-Living

IEEE Sensors Journal, 2021

Objective detection of periods of wear and non-wear is critical for human studies that rely on in... more Objective detection of periods of wear and non-wear is critical for human studies that rely on information from wearable sensors, such as food intake sensors. In this paper, we present a novel method of compliance detection on the example of the Automatic Ingestion Monitor v2 (AIM-2) sensor, containing a tri-axial accelerometer, a still camera, and a chewing sensor. The method was developed and validated using data from a study of 30 participants aged 18-39, each wearing the AIM-2 for two days (a day in pseudo-free-living and a day in free-living). Four types of wear compliance were analyzed: ‘normal-wear’, ‘non-compliant-wear’, ‘non-wear-carried’, and ‘non-wear-stationary’. The ground truth of those four types of compliance was obtained by reviewing the images of the egocentric camera. The features for compliance detection were the standard deviation of acceleration, average pitch, and roll angles, and mean square error of two consecutive images. These were used to train three random forest classifiers 1) accelerometer-based, 2) image-based, and 3) combined accelerometer and image-based. Satisfactory wear compliance measurement accuracy was obtained using the combined classifier (89.24%) on leave one subject out cross-validation. The average duration of compliant wear in the study was 9h with a standard deviation of 2h or 70.96% of total on-time. This method can be used to calculate the wear and non-wear time of AIM-2, and potentially be extended to other devices. The study also included assessments of sensor burden and privacy concerns. The survey results suggest recommendations that may be used to increase wear compliance.

Objective Detection of Cigarette Smoking from Physiological Sensor Signals

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019

Cigarette smoking has severe health impacts on those who smoke and the people around them. Severa... more Cigarette smoking has severe health impacts on those who smoke and the people around them. Several wearable sensing modalities have recently been investigated to collect objective data on daily smoking, including detection of smoking episodes from breathing patterns, hand to mouth behavior, and characteristic hand gestures or cigarette lighting events. In order to provide new insight into ongoing research on the objective collection of smoking-related events, this paper proposes a novel method to identify smoking events from the associated changes in heart rate parameters specific to smoking. The proposed method also accounts for the breathing rate and body motion of the person who is smoking to better distinguish these changes from intense physical activities. In this research, a human study was first performed on 20 daily cigarette smokers to record heart rate, breathing rate, and body acceleration collected from a wearable chest sensor consisting of an ECG and bioimpedance measurement sensor and a 3D inertial sensor. Each participant spent ~2 hours in a laboratory environment (mimicking daily activities that included smoking 4 cigarettes) and ~24 hours under unconstrained free-living conditions. A support vector machine-based classifier was developed to automatically detect smoking episodes from the captured sensor signals using fifteen features selected by a forward-feature selection method. In a leave one subject out cross-validation, the proposed approach detected smoking events (187 out of total 232) with the sensitivity and F-score accuracy of 0.87 and 0.79, respectively, in the laboratory setting (known activities) and 0.77 and 0.61, respectively, under free-living conditions. These results validate the proof-of-concept that, although further research is necessary for performance improvement, characteristic changes in heart rate parameters could be a useful indicator of cigarette smoking even under free-living conditions.

Quantitative assessment of nutritive sucking patterns in preterm infants

Early Human Development, 2020

OBJECTIVE To assess patterns of nutritive sucking in very preterm infants ≤32 weeks of gestation.... more OBJECTIVE To assess patterns of nutritive sucking in very preterm infants ≤32 weeks of gestation. STUDY DESIGN Very preterm infants who attained independent oral feeding were prospectively assessed with an instrumented feeding bottle that measures nutritive sucking. The primary outcome measure was nutritive sucking performance at independent oral feeding. RESULT We assessed nutritive sucking patterns in 33 very preterm infants. We recorded 63 feeding sessions. The median number of sucks was 784 (IQR: 550-1053), the median sucking rate was 36/min (IQR: 27-55), and the median number of sucking bursts during the first 5 min of oral feeding was 14 (IQR: 12-16). Maximum sucking strength correlated with the number of sucks (r = 0.62; p < 0.01). No safety concerns were identified during the study. CONCLUSION The quantitative analysis of nutritive sucking patterns with a newly developed instrumented bottle in stable, very preterm infants is safe and feasible. More research is needed to develop and refine the instrument further.

Quantitative Assessment of Nutritive Sucking Patterns in Preterm Infants (P11-095-19)

Current Developments in Nutrition, 2019

Objectives Mastery of oral feeding independent of supplementary feeding tubes is crucial for safe... more Objectives Mastery of oral feeding independent of supplementary feeding tubes is crucial for safe discharge of very preterm infants (≤ 32 weeks of gestation). Qualitative assessments of suck-swallow-breathe coordination can be subjective and result in prolonged hospitalizations and delay discharges. Quantitative assessment of sucking activity during feeding (i.e., nutritive sucking) could improve determination of readiness for progression to independent oral feeding and facilitate discharge planning. To quantitively assess nutritive sucking patterns using an instrumented feeding bottle. Methods Preterm infants ≤ 32 weeks of gestation who attained independent oral feeding were prospectively evaluated after written informed consent was obtained. Infants with congenital anomalies and infants with major comorbidities were excluded. Nutritive sucking was assessed for a minimum of one feeding session in each participant by instrumenting a commercially available feeding bottle with a detac...

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Smoking detection based on regularity analysis of hand to mouth gestures

Biomedical Signal Processing and Control, 2019

A number of studies have been introduced for the detection of smoking via a variety of features e... more A number of studies have been introduced for the detection of smoking via a variety of features extracted from the wrist IMU data. However, none of the previous studies investigated gesture regularity as a way to detect smoking events. This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures. Here, the regularity of hand gestures was estimated from a one axis accelerometer worn on the wrist of the dominant hand. To quantify the regularity score, this paper applied a novel approach of unbiased autocorrelation to process the temporal sequence of hand gestures. The comparison of regularity score of smoking events with other activities substantiated that hand-to-mouth gestures are highly regular during smoking events and have the potential to detect smoking from among a plethora of daily activities. This hypothesis was validated on a dataset of 140 cigarette smoking events generated by 35 regular smokers in a controlled setting. The regularity of gestures detected smoking events with an F1-score of 0.81. However, the accuracy dropped to 0.49 in the free-living study of same 35 smokers smoking 295 cigarettes. Nevertheless, regularity of gestures may be useful as a supportive tool for other detection methods. To validate that proposition, this paper further incorporated the regularity of gestures in an instrumented lighter based smoking detection algorithm and achieved an improvement in F1-score from 0.89 (lighter only) to 0.91 (lighter and regularity of hand gestures).

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Automatic Recognition of Activities of Daily Living Utilizing Insole-Based and Wrist-Worn Wearable Sensors

IEEE Journal of Biomedical and Health Informatics, 2017

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Development of the RT-GAIT, a Real-Time feedback device to improve Gait of individuals with stroke

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015

Regaining the ability to walk is a major rehabilitation goal after a stroke. Recent research sugg... more Regaining the ability to walk is a major rehabilitation goal after a stroke. Recent research suggests that, in people with stroke, task-oriented and intensive rehabilitation strategies can drive cortical reorganization and increase activity levels. This paper describes development and pilot testing of a novel wearable device for Real-Time Gait and Activity Improving Telerehabilitation (RT-GAIT), designed for use with such rehabilitation strategies. The RT-GAIT provides auditory or tactile feedback to the individual wearing the platform. The feedback is based on the amount of time spent in stance phase on each foot, as measured by the pressure sensors embedded into the insoles. The system was initially bench-validated using sensor signals collected in a previous study. Next, a clinical case study was conducted with one post-stroke individual. The results of the case study suggest that the RT-GAIT device can potentially improve the gait parameters. Mean difference in stance times betw...

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