In recent years, there has been a growing interest in smart glasses (SG) incorporating augmented reality (AR) technology. The most remarkable feature of SG is that users can execute tasks with both hands while getting necessary information while without taking their eyes off the task at hand. Such hands-free (HF) access to information can help prevent contamination. We are engaged in developing HF manuals for medical applications using SG. Use of SG could be expected to be associated with eye strain and headaches, but these could also occur when the users read manuals displayed on a LCD display (PC) or manuals printed on paper (Paper). In order to compare the effects of presentation of manuals in different modalities, 77 randomly selected students and faculty members were assigned to read HF (n=27), PC (n=40), and Paper (n=10) manual. The degree of eye strain induced by each presentation was compared by the flicker value, blinking frequency, high frequency component (HFC), pulse rate and “subjective symptoms”1), before and after loading for an hour. The results revealed less fatigue in the group assigned to HF as compared to the other groups. Therefore, there are no major obstacles to HF manual development.

We verified that arteriovenous fistula (AVF) function can be evaluated from shunt sound using a body-conducted sound sensor (BCS) with a wide-band frequency characteristic. In the BCS, we compared two types of sensors: an air-coupled microphone (ACM) and an acceleration sensor (Type 8001). Evaluation for sensors were performed for 36 patients. We also developed a shunt-sound observation system and evaluated it using the spectrogram and power spectrum values obtained using a sound spectrometer. Furthermore, we verified that two indices of ultrasonography information, namely, the resistance index (RI) and flow volume (FV), can be estimated from the shunt sound and classified through machine learning. The BCS detected the shunt sound signal in the spectrogram over a wide frequency band. The sensitivities of the BCS in the non-amplified detection of stenosis sound signals were 0.93/0.92 (below 1 kHz/above 1 kHz, respectively), surpassing those of other sensors(ACM: 0.82/0.77, Type 8001: 0.82/0.25). Furthermore, the highest machine learning accuracy was obtained using BCS in the classification segments of RI > 0.60, 0.65, and 0.70 and FV < 350 ml/min. BCS is particularly suitable for detecting stenosis sounds of over 1 kHz and estimating RI and is considered to be suitable for functional evaluations of AVF.