Exemplary implementations may: obtain passenger information related to a passenger that is relevant to an upcoming medical appointment; store such passenger information; and transmit such passenger information wirelessly to a caregiver with which the upcoming medical appointment is scheduled. Exemplary implementation also may: generate output signals conveying passenger information; provide one or more automated robotic assistants; determine the passenger information; determine necessity of deployment of the one or more automated robotic assistants; and deploy, based on the necessity of deployment, the one or more automated robotic assistants.

A position identifying unit is configured to identify the location of a monitored person in the horizontal direction and maximum height of the monitored person in the vertical direction on the basis of top-down image data acquired by an upper image sensor provided above a monitored area capturing an image of the monitored area and lateral image data acquired by a lateral image sensor provided along a side of the monitored area capturing an image of the monitored area. An event determining unit assesses a fall-related event for the monitored person on the basis of a location in the horizontal direction, a maximum height in the vertical direction.

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.

A method for monitoring a patient undergoing therapy includes wirelessly tracking a motion of the patient over time to generate a sequence of data representing the motion of the patient over time, processing the sequence of data representing the motion of the patient over time using a biometric parameter extraction module to extract data representing one or more biometric parameters, determining an estimated response of the patient to the therapy based at least in part of the data representing one or more biometric parameters, and providing a characterization of the estimated response of the patient to the therapy to one or more of a caretaker, the patient, or a clinician.

A bio-information detecting sensor according to an embodiment of the present invention includes a flexible substrate, light emitting parts disposed on the flexible substrate, and a light receiving part disposed on the flexible substrate and having a donut shape surrounding the light emitting parts.

Various embodiments disclosed relate to a resonator system that can be used to monitor status of tissue. A passive resonant sensor having an inductive element and a capacitive element can be implemented in a patch format and attached to tissue. The patch and resonant sensor can be structured such that, when contacting the tissue, dielectric of the contacted tissue contributes to capacitance of the resonant sensor to affect a resonant frequency of the resonant sensor. Additional apparatus, systems, and methods can be implemented with variations in a patch-resonant sensor structure.

A measuring method determines the length conditions, the position and/or the radius of movement of the lower extremities of a bed-bound patient. A knee module of a rehabilitation mechanism is connected to a point of application of a leg of the patient. A measuring device records and determines a trajectory solely of the point of application during a movement of the leg by the knee module. A model generation device creates a kinematic model from the data. This allows a determination of the length conditions, the position and/or the radius of movement during an initializing movement without having to undertake any surgical intervention. This does away with the need for complex sensor technology and provides a kinematic model of the radius of movement of the patient, the parameters of which kinematic model are used for establishing new therapeutic procedures within the patient's physical therapy.

A sensorimotor exercise device including a casing is provided. The casing includes an elongated center member having a first end and a second end, a first end member provided at the first end of the center member and a second end member provided at the second end of the center member. The casing also includes a low viscosity fluid at least partially filing a hollow interior of the sensorimotor exercise device. The center member has a cylindrical shape and the first and second end members have a tapered configuration. A diameter of each of the first and second end members increasing from a first end thereof toward an opposing second end thereof.

A pair of electronic shoe insoles aids an individual with peripheral neuropathy in walking without falling, despite the user having little or no sensation in her feet. Each insole uses a number of pressure sensors and provides various forms of biofeedback to the user such as auditory, haptic, and vibratory feedback which corresponds to the position of the user's foot on the ground. Vibration feedback is provided through vibration motors disposed against the soles of the user's feet at selected locations which correspond to locations of pressure sensors. This allows for direct neural stimulation of the sole of the foot at three biomechanically appropriate locations. Auditory and haptic feedback are provided through auxiliary devices that the user wears on appropriate parts of the body. Biofeedback transmitted through these mechanisms would correspond to change in foot position as detected by the pressure sensors. The shoe insoles may provide one or more of these forms of feedback, and other types of feedback may be provided by output devices as well. An embedded microcontroller wirelessly connected to a computer, tablet or phone permits an individual to monitor gait performance and to adjust numerous parameters of this biofeedback mechanism, such as time delays and strength of vibration or audio feedback. The device may also include a driving mode in which small variations of pressure on the gas pedal would be conveyed to the user through haptic and auditory feedback, thereby allowing the user to drive.

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.