ALBERT

Description: Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prevent electrode displacement. This makes it difficult for users to wear such measurement systems. A less restraining wearable system, wherein the electrodes are placed around the ankle, is realized for target muscles widely distributed around the shank. The signals obtained from around the ankle are propagated biosignals from several muscles, and are referred to as distal EMG signals. Our objective is to develop a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals. We propose a signal processing method based on multiple bandpass filters from the perspectives of noise separation and feature augmentation. We conducted an experiment for designing the hardware configuration, and three other experiments for evaluating the estimation accuracy and dependability of muscle activity analysis. Compared to the baseline based on a 20-500 Hz bandpass filter, the results indicated that the proposed system estimates muscle activity with higher accuracy. Experimental results suggest that lower leg muscle activity can be estimated using distal EMG signals.

Description: Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.

Description: Label-free evaluation and monitoring of living cell conditions or functions by means of chemical and/or physical sensors in a real-time manner are increasingly desired in the field of basic research of cells and clinical diagnosis. In order to perform multi-parametric analysis of living cells on a chip, we here developed a surface plasmon resonance (SPR) imaging (SPRI)-impedance sensor that can detect both refractive index (RI) and impedance changes on a sensor chip with comb-shaped electrodes. We then investigated the potential of the sensor for label-free and real-time analysis of living cell reactions in response to stimuli. We cultured rat basophilic leukemia (RBL)-2H3 cells on the sensor chip, which was a glass slide coated with comb-shaped electrodes, and detected activation of RBL-2H3 cells, such as degranulation and morphological changes, in response to a dinitro-phenol-conjugated human serum albumin (DNP-HSA) antigen. Moreover, impedance analysis revealed that the changes of impedance derived from RBL-2H3 cell activation appeared in the range of 1 kHz–1 MHz. Furthermore, we monitored living cell-derived RI and impedance changes simultaneously on a sensor chip using the SPRI-impedance sensor. Thus, we developed a new technique to monitor both impedance and RI derived from living cells by using a comb-shaped electrode sensor chip. This technique may enable us to clarify complex living cell functions which affect the RI and impedance and apply this to medical applications, such as accurate clinical diagnosis of type I allergy.

Description: It is difficult for visually impaired people to move indoors and outdoors. In 2018, world health organization (WHO) reported that there were about 253 million people around the world who were moderately visually impaired in distance vision. A navigation system that combines positioning and obstacle detection has been actively researched and developed. However, when these obstacle detection methods are used in high-traffic passages, since many pedestrians cause an occlusion problem that obstructs the shape and color of obstacles, these obstacle detection methods significantly decrease in accuracy. To solve this problem, we developed an application “Follow me!”. The application recommends a safe route by machine learning the gait and walking route of many pedestrians obtained from the monocular camera images of a smartphone. As a result of the experiment, pedestrians walking in the same direction as visually impaired people, oncoming pedestrians, and steps were identified with an average accuracy of 0.92 based on the gait and walking route of pedestrians acquired from monocular camera images. Furthermore, the results of the recommended safe route based on the identification results showed that the visually impaired people were guided to a safe route with 100% accuracy. In addition, visually impaired people avoided obstacles that had to be detoured during construction and signage by walking along the recommended route.

Description: Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.

Description: Label-free evaluation and monitoring of living cell conditions or functions by means of chemical and/or physical sensors in a real-time manner are increasingly desired in the field of basic research of cells and clinical diagnosis. In order to perform multi-parametric analysis of living cells on a chip, we here developed a surface plasmon resonance (SPR) imaging (SPRI)-impedance sensor that can detect both refractive index (RI) and impedance changes on a sensor chip with comb-shaped electrodes. We then investigated the potential of the sensor for label-free and real-time analysis of living cell reactions in response to stimuli. We cultured rat basophilic leukemia (RBL)-2H3 cells on the sensor chip, which was a glass slide coated with comb-shaped electrodes, and detected activation of RBL-2H3 cells, such as degranulation and morphological changes, in response to a dinitro-phenol-conjugated human serum albumin (DNP-HSA) antigen. Moreover, impedance analysis revealed that the changes of impedance derived from RBL-2H3 cell activation appeared in the range of 1 kHz–1 MHz. Furthermore, we monitored living cell-derived RI and impedance changes simultaneously on a sensor chip using the SPRI-impedance sensor. Thus, we developed a new technique to monitor both impedance and RI derived from living cells by using a comb-shaped electrode sensor chip. This technique may enable us to clarify complex living cell functions which affect the RI and impedance and apply this to medical applications, such as accurate clinical diagnosis of type I allergy.

Description: Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prevent electrode displacement. This makes it difficult for users to wear such measurement systems. A less restraining wearable system, wherein the electrodes are placed around the ankle, is realized for target muscles widely distributed around the shank. The signals obtained from around the ankle are propagated biosignals from several muscles, and are referred to as distal EMG signals. Our objective is to develop a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals. We propose a signal processing method based on multiple bandpass filters from the perspectives of noise separation and feature augmentation. We conducted an experiment for designing the hardware configuration, and three other experiments for evaluating the estimation accuracy and dependability of muscle activity analysis. Compared to the baseline based on a 20-500 Hz bandpass filter, the results indicated that the proposed system estimates muscle activity with higher accuracy. Experimental results suggest that lower leg muscle activity can be estimated using distal EMG signals.