Haik Kalantarian

Monitoring eating habits using a piezoelectric sensor-based necklace

Maintaining appropriate levels of food intake and developing regularity in eating habits is crucial to weight loss and the preservation of a healthy lifestyle. Moreover, awareness of eating habits is an important step towards portion control and weight loss. In this paper, we introduce a novel food-intake monitoring system based around a wearable wireless-enabled necklace. The proposed necklace includes an embedded piezoelectric sensor, small Arduino-compatible microcontroller, Bluetooth LE transceiver, and Lithium-Polymer battery. Motion in the throat is captured and transmitted to a mobile application for processing and user guidance. Results from data collected from 30 subjects indicate that it is possible to detect solid and liquid foods, with an F-measure of 0.837 and 0.864, respectively, using a naive Bayes classifier. Furthermore, identification of extraneous motions such as head turns and walking are shown to significantly reduce the false positive rate of swallow detection.

A Wearable Nutrition Monitoring System

Maintaining appropriate levels of food intake anddeveloping regularity in eating habits is crucial to weight lossand the preservation of a healthy lifestyle. Moreover, maintainingawareness of ones own eating habits is an important steptowards portion control and ultimately, weight loss. Though manysolutions have been proposed in the area of physical activitymonitoring, few works attempt to monitor an individuals foodintake by means of a noninvasive, wearable platform. In thispaper, we introduce a novel nutrition-intake monitoring systembased around a wearable, mobile, wireless-enabled necklacefeaturing an embedded piezoelectric sensor. We also propose aframework capable of estimating volume of meals, identifyinglong-term trends in eating habits, and providing classificationbetween solid foods and liquids with an F-Measure of 85% and86% respectively. The data is presented to the user in the formof a mobile application.

Audio-based detection and evaluation of eating behavior using the smartwatch platform

In recent years, smartwatches have emerged as a viable platform for a variety of medical and health-related applications. In addition to the benefits of a stable hardware platform, these devices have a significant advantage over other wrist-worn devices, in that user acceptance of watches is higher than other custom hardware solutions. In this paper, we describe signal-processing techniques for identification of chews and swallows using a smartwatch device׳s built-in microphone. Moreover, we conduct a survey to evaluate the potential of the smartwatch as a platform for monitoring nutrition. The focus of this paper is to analyze the overall applicability of a smartwatch-based system for food-intake monitoring. Evaluation results confirm the efficacy of our technique; classification was performed between apple and potato chip bites, water swallows, talking, and ambient noise, with an F-measure of 94.5% based on 250 collected samples.

Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor

Food intake levels, hydration, ingestion rate, and dietary choices are all factors known to impact the risk of obesity. This paper presents a novel wearable system in the form of a necklace, which aggregates data from an embedded piezoelectric sensor capable of detecting skin motion in the lower trachea during ingestion. The skin motion produces an output voltage with varying frequencies over time. As a result, we propose an algorithm based on time-frequency decomposition, spectrogram analysis of piezoelectric sensor signals, to accurately distinguish between food types, such as liquid and solid, hot and cold drinks, and hard and soft foods. The necklace transmits data to a smartphone, which performs the processing of the signals, classifies the food type, and provides visual feedback to the user to assist the user in monitoring their eating habits over time. We compare our spectrogram analysis with other time-frequency features, such as matching pursuit and wavelets. Experimental results demonstrate promise in using time-frequency features, with high accuracy of distinguishing between food categories using spectrogram analysis and extracting key features representative of the unique swallow patterns of various foods.

A smartwatch-based medication adherence system

Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting adherence to prescription medication based the identification of several motions using the built-in tri-axial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.

Detection of Gestures Associated With Medication Adherence Using Smartwatch-Based Inertial Sensors

Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting several motions that may be predictors of medication adherence, using built-in triaxial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.

A wearable sensor system for medication adherence prediction

OBJECTIVE Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from low accuracy. Our objective is to develop and test a novel system for assessment of medication adherence. METHODS Recently, several smart pill bottle technologies have been proposed, which can detect when the bottle has been opened, and even when a pill has been retrieved. However, very few systems can determine if the pill is subsequently ingested or discarded. We propose a system for detecting user adherence to medication using a smart necklace, capable of determining if the medication has been ingested based on the skin movement in the lower part of the neck during a swallow. This, coupled with existing medication adherence systems that detect when medicine is removed from the bottle, can detect a broader range of use-cases with respect to medication adherence. RESULTS Using Bayesian networks, we were able to correctly classify between chewable vitamins, saliva swallows, medication capsules, speaking, and drinking water, with average precision and recall of 90.17% and 88.9%, respectively. A total of 135 instances were classified from a total of 20 subjects. CONCLUSION Our experimental evaluations confirm the accuracy of the piezoelectric necklace for detecting medicine swallows and disambiguating them from related actions. Further studies in real-world conditions are necessary to evaluate the efficacy of the proposed scheme.

Non-invasive detection of medication adherence using a digital smart necklace

Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and a host of other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from clear drawbacks such as high complexity, user burden, and low accuracy. In this paper, we propose a two step system for detecting user adherence to medication. First, force-sensitive resistors are used to determine when the pill bottle has been opened. Subsequently, medication ingestion is detected using a smart necklace equipped with a piezoelectric sensor. Evaluations confirm high accuracy of the proposed technique.

Non-invasive monitoring of eating behavior using spectrogram analysis in a wearable necklace

Food intake levels, hydration, chewing and swallowing rate, and dietary choices are all factors known to impact ones health. This paper presents a novel wearable system in the form of a necklace, which aggregates data from an embedded piezoelectric sensor capable of detecting skin motion in the lower trachea during ingestion. We propose an algorithm based on spectrogram analysis of piezoelectric sensor signals to accurately distinguish between food types such as liquid and solid, hot and cold drinks and hard and soft foods. The necklace transmits data to a smartphone, which performs the processing of the signals, classifies the food type, and provides visual feedback to the user to assist the user in monitoring their eating habits over time. Experimental results demonstrate high classification accuracy of the proposed method, and validate the use of a spectrogram in extracting key features representative of the unique swallow patterns of various foods.

Temperature and humidity calibration of a low-cost wireless dust sensor for real-time monitoring

This paper introduces the design, calibration, and validation of a low-cost portable sensor for the real-time measurement of dust particles within the environment. The proposed design consists of low hardware cost and calibration based on temperature and humidity sensing to achieve accurate processing of airborne dust density. Using commercial particulate matter sensors, a highly accurate air quality monitoring sensor was designed and calibrated using real world variations in humidity and temperature for indoor and outdoor applications. Furthermore, to provide a low-cost secure solution for real-time data transfer and monitoring, an onboard Bluetooth module with AES data encryption protocol was implemented. The wireless sensor was tested against a Dylos DC1100 Pro Air Quality Monitor, as well as an Alphasense OPC-N2 optical air quality monitoring sensor for accuracy. The sensor was also tested for reliability by comparing the sensor to an exact copy of itself under indoor and outdoor conditions. It was found that accurate measurements under real-world humid and temperature varying and dynamically changing conditions were achievable using the proposed sensor when compared to the commercially available sensors. In addition to accurate and reliable sensing, this sensor was designed to be wearable and perform real-time data collection and transmission, making it easy to collect and analyze data for air quality monitoring and real-time feedback in remote health monitoring applications. Thus, the proposed device achieves high quality measurements at lower-cost solutions than commercially available wireless sensors for air quality.