A sarcopenia evaluation method in a sarcopenia evaluation device that evaluates sarcopenia based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one walking parameter of an angle of a knee joint of one leg in a stance phase of the one leg of the subject, an angle of the knee joint of the one leg in a swing phase of the one leg, a vertical displacement of a toe of one foot in the stance phase, a vertical displacement of the toe of the one foot in the swing phase, an angle of an ankle joint of the one foot in the stance phase, and an angle of the ankle joint of the one foot in the swing phase; and determining whether or not the subject has sarcopenia using at least one walking parameter.

An image detection method for determining positions of a user. According to the image detection method, a plurality of images of the user are obtained, whether the user moves is determined according to the images, a plurality of feature parameters of the plurality of images are obtained, and a body distribution analysis and a face occlusion analysis are performed to determine the position of the user.

In one example, a sensor system of a patient monitoring system includes a voiding sensor that includes an electrical circuit configured to be transitioned from a first impedance state to a second impedance state in response to a voiding event in an absorbent region of an absorbent article worn by a patient. The sensor system also includes one or more processors configured to: determine, based on whether the impedance state of the electrical circuit is open or closed, that the voiding event has occurred; and generate, based at least in part on the occurrence of the voiding event, patient status data of the patient. The sensor system also includes a communication module configured to wirelessly transmit, via a relay module, the patient status data to a remote monitoring device that is distinct from the relay module.

Provided is a 3D bone density and bone age calculation apparatus using an artificial intelligence-based rotation manner. The 3D bone density and bone age calculation apparatus includes a main body, and the main body includes a rotary drum including a drum shaft gear, an X-ray generator, an intensifying screen and an image data capturer, a drum driver including a motor shaft gear connected to the drum shaft gear so as to rotate the rotary drum, a motor, support rollers and one of an origin sensor and an encoder, outer and inner cases, front and rear cases, a capturing holder, and a controller including a position selector configured to select an image-captured position of the rotary drum, an input unit configured to input a current age, sex and nutritional status of a patient, etc., and a display configured to display captured images and a diagram indicating bone age.

A drug delivery device includes a capsule, a logic circuit disposed within the capsule, and an actuator connected to the logic circuit and configured to expose an interior of the capsule to an exterior of the capsule upon activation by the logic circuit.

A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject thereon, and one or more data processing devices operatively coupled to the force measurement assembly. In one or more embodiments, the one or more data processing devices are operatively coupled to the force measurement assembly, the one or more data processing devices configured to receive one or more signals that are representative of forces and/or moments being applied to a top surface of the force measurement assembly by the subject, and to convert the one or more signals into output forces and/or moments, the one or more data processing devices further configured to predict one or more balance parameters of the subject using a trained neural network.

Provided are: an acquisition unit that acquires physical state information indicating physical states of the people from one or more sensors that detect at least the people; a physical function analysis unit that analyzes a change in physical functions of the people based on a time-series change of the physical state information acquired by the acquisition unit; and a physical function improvement proposing unit that generates and outputs physical function improvement proposal information indicating an improvement proposal of the physical function with respect to the change in the physical functions of the people based on an analysis result of the physical function analysis unit.

A home automation (HA) system may include a cloud server, HA operation devices within a senior living facility, and HA user interface devices for respective users within the senior living facility. Each HA user interface device may include a user input device, a display defining a user interface (UI), and a controller. The HA system may include HA hub devices within the senior living facility to provide communications for the cloud server, the HA user interface devices, and the HA operation devices. The controller may send user interaction data to the cloud server and operate the UI according to a user cognitive level. The cloud server may be configured to determine the user cognitive level based upon the user interaction data received from a given HA user interface device, and send the user cognitive level to the given HA user interface device.

The present invention includes a method and apparatus providing an ongoing and real time indicator for prediction of health conditions during the usual lifetime of a person by providing an ongoing and real time indicator for predicting remaining lifetimes for one or more patients comprising: providing a monitoring system connected to the cloud, a Wi-Fi or Bluetooth network which is connected to a wearable device; and providing a wearable device which contains one or more accelerometers, temperature monitoring devices, EKG monitoring devices and other useful devices; wherein the wearable device is powered internally by a battery or other such appropriate energy sources.

A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.