Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F1 score of the classification was about 94.0% and the accuracy above 89%. We believe this approach will be helpful for designing a non-intrusive and un-obtrusive eyewear-based ingestive behavior monitoring system.

Microscopic observations of cultured cells in many lab-on-a-chip applications mostly utilize digital image acquisition using CCD sensors connected to a personal computer. The functionalities of this digital imaging can be enhanced by implementing computer-vision based augmented reality technologies. In this study, we present a new method for precisely relocating biological specimens under microscopic inspections by using augmented reality patterns, called microscopic augmented reality indicators (μ-ARIs). Since the method only requires sticky films attached under sample containers of any shape, long-term live cell observations can be conducted at much less extra cost than with conventional methods. On these sticky films, multiple arrays of position-indicating patterns were imprinted to provide a reference coordinate system for recording and relocating the accurate position and rotation of the specimen under inspection. This approach can be useful for obtaining the exact locations of individual cells inside biological samples using μ-ARI imprinted transparent films in a rapid and controlled manner.

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.

No abstract

No abstract