Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Summary

This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

Abstract

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. We fabricated a 3D-printed frame of the glasses and a load cell-integrated printed circuit board (PCB) module inserted in both hinges of the frame. The module was used to acquire the force signals, and transmit them wirelessly. These procedures provide the system with higher mobility, which can be evaluated in practical wearing conditions such as walking and waggling. A performance of the classification is also evaluated by distinguishing the patterns of food intake from those physical activities. A series of algorithms were used to preprocess the signals, generate feature vectors, and recognize the patterns of several featured activities (chewing and winking), and other physical activities (sedentary rest, talking, and walking). The results showed that the average F₁ score of the classification among the featured activities was 91.4%. We believe this approach can be potentially useful for automatic and objective monitoring of ingestive behaviors with higher accuracy as practical means to treat ingestive problems.

Introduction

Continuous and objective monitoring of food intake is essential for maintaining energy balance in the human body, as excessive energy accumulation may cause overweightness and obesity¹, which could result in various medical complications². The main factors in the energy imbalance are known to be both excessive food intake and insufficient physical activity³. Various studies on the monitoring of daily energy expenditure have been introduced with automatic and objective measurement of physical activity patterns through wearable devices^4,5

Protocol

NOTE: All the procedures including the use of human subjects were accomplished by a non-invasive manner of simply wearing a pair of glasses. All the data were acquired by measuring the force signals from load cells inserted in the glasses that were not in direct contact with the skin. The data were wirelessly transmitted to the data recording module, which, in this case is a designated smartphone for the study. All the protocols were not related to in vivo/in vitro human studies. No drug and blood samples were used for the experiments. Informed consent was obtained from all subjects of the experiments.

1. Manufacturing of a ....

Results

Through the procedures outlined in the protocol, we prepared two versions of the 3D printed frame by differentiating the length of the head piece, L_H (133 and 138 mm), and the temples, L_T (110 and 125 mm), as shown in Figure 4. Therefore, we can cover several wearing conditions, which can be varied from the subjects' head size, shape, etc. The subjects chose one of the frames to fit to their head for the user study. The vertical.......

Discussion

In this study, we first proposed the design and manufacturing process of glasses that sense the patterns of food intake and physical activities. As this study mainly focused on the data analysis to distinguish the food intake from the other physical activities (such as walking and winking), the sensor and data acquisition system required the implementation of mobility recording. Thus, the system included the sensors, the MCU with wireless communication capability, and the battery. The proposed protocol provided a novel a.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
FSS1500NSB	Honeywell, USA		Load cell
INA125U	Texas Instruments, USA		Amplifier
ESP-07	Shenzhen Anxinke Technology, China		MCU with Wi-Fi module
74LVC1G3157	Nexperia, The Netherlands		Multiplexer
MP701435P	Maxpower, China		LiPo battery
U1V10F3	Pololu, USA		Voltage regulator
Ultimaker 2	Ultimaker, The Netherlands		3D printer
ColorFabb XT-CF20	ColorFabb, The Netherlands		Carbon fiber filament
iPhone 6s Plus	Apple, USA		Data acquisition device
Jelly Belly	Jelly Belly Candy Company, USA		Food texture for user study