Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning.

Background: Various sensor technologies have been developed to monitor the health of older adults; however, most of them require attachment to the skin. This study aimed to develop a health monitoring system, using a non-adhesive, non-invasive polyvinylidene difluoride piezoelectric sensor, with the patient being able to lead a normal daily life without being conscious of the sensor. The vibration signal from the human body surface obtained by the piezoelectric sensor, which is a ballistocardiography signal, contains information on the person's heart and respiratory rates.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals.

Efficacy of ultraviolet light-emitting diodes (UV-LED) at four different peak wavelengths against Cryptosporidium parvum oocysts by inactivation assay using immunodeficient mice.

As an alternative to using ultraviolet (UV) lamps, which are made with mercury that is toxic to the environment and human health, UV light-emitting diodes (UV-LEDs) are expected to be effective for inactivating microorganisms in water. Although UV-LEDs have been reported to be effective against bacteria and viruses, the effectiveness of UV-LEDs against Cryptosporidium parasites has not been fully evaluated. As we report here, we have developed an in vivo quantitative inactivation assay for C.