The present disclosure provides methods and apparatuses for determining a fatigue index. A method of determining a fatigue index may include receiving physiological signals, generating a plurality of parameters of heart rate variability based on the physiological signals, and determining the fatigue index based on the plurality of parameters of heart rate variability.

A wearable apparatus selects at least two signal channels corresponding to an arterial signal, the channels associated with corresponding optical sensors of the wearable apparatus. Data is obtained from signal channels over a predetermined time. A function is applied to the data to transform the data to a frequency domain. Phase values are determined for frequency components of the data in the frequency domain. A phase difference value is determined between the phase values. A time shift value is determined between the data based on the phase difference value. A modified pulse transmit time is determined, based on the time shift value, representing a transit time for a pressure wavefront to travel between optical sensors. A pulse wave velocity is determined based on the modified pulse transit time. A blood pressure value is calculated based on the pulse wave velocity. A message is provided based on the blood pressure value.

Systems and methods for monitoring a range of motion of a joint are described. For example, in one embodiment, a first set of sensors may sense accelerations of a first body portion located on a first side of the joint and a second set of sensors may sense accelerations of the second body portion located on a second opposing side of the joint. The acceleration data may then be used to compute the relative motion of the first and second body portions to determine movement of the joint. This joint movement may then be used to determine one or more range of motion movement metrics which are output for viewing by a subject or medical practitioner.

Physiological monitoring can be provided through a syncope sensor imbedded into an electrocardiography monitor, which correlates syncope events and electrocardiographic data. Physiological monitoring can be provided through a lightweight wearable monitor that includes two components, a flexible extended-wear electrode patch and a reusable monitor recorder that removably snaps into a receptacle on the electrode patch. The wearable monitor sits centrally on the patient's chest at the sternal midline and includes a unique narrow “hourglass”-like shape, significantly improving the ability of the monitor to cutaneously sense cardiac electrical potential signals, particularly the P-wave and QRS interval signals, which indicate ventricular activity in electrocardiographic waveforms. The electrocardiographic electrodes on the electrode patch are tailored for axial positioning along the midline of the sternum to capture action potential propagation in an orientation that corresponds to the aVF lead in a conventional 12-lead electrocardiogram, which senses positive P-waves.

A method and system for monitoring a retail environment by performing video content analysis based on two-dimensional image data and depth data are disclosed. Accuracy in customer actions to provide assistance, change marketing behavior, safety and theft, for example, is increase by analyzing video containing two-dimensional image data and associated depth data. Height data may be obtained from depth data to assist in object detection, object classification (e.g., detection a customer or inventory) and/or event detection.

Provided is a respiratory rate measurement device (4), the respiratory rate measurement device (4) including a detection unit (6) that detects an in-tube pressure and/or an in-tube gas flow rate in a tube (2) supplying concentrated oxygen gas to a patient from a pressure swing adsorption oxygen concentration device (1) connected to the patient and concentrating oxygen in the air by periodically repeating pressurization and depressurization, and outputs pressure data and/or gas flow rate data, an arithmetic operation unit (722) that extracts patient respiratory information data, based on the pressure data and/or the gas flow rate data, and an estimation unit (723) that estimates a respiratory rate per predetermined time, based on the patient respiratory information data, wherein the estimation unit (723) estimates the respiratory rate using, as a respiratory interval, a time Δt in which the autocorrelation coefficient takes a peak.

A system for treatment of obstructive sleep apnea (OSA) is described. The system includes an introducer needle having an elongated body. The introducer needle is configured to create an opening in a tongue of a patient for implantation of a lead for treating OSA. One or more electrically conductive areas are located on the elongated body. A medical device is configured to deliver a stimulation signal via the introducer needle through the one or more electrically conductive areas to the tongue of the patient to stimulate one or more motor points of a protrusor muscle within the tongue of the patient.

A computer-implemented system includes a treatment apparatus configured to be manipulated by a patient while performing a treatment plan and a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan. The server computing device receives information pertaining to the patient, generates, based on the information, the treatment plan including instructions for the patient to follow, and receives a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The server computing device generates, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The server computing device transmits the treatment plan and the billing sequence to a computing device.

Systems and methods of the present disclosure include a five-layer environment consisting tremor management hardware, interface, control system, machine learning environment and cloud capabilities. These elements come together to implement program instructions and perform steps to: receive sensory data elements(s) for tremor stat(s), physiological and/or environmental state(s) of the user, user activities and state(s) of the tremor management hardware and based on these state(s) generate at least one actionable tremor mitigation action associated with historical tremor mitigation improvements recorded in a memory, and display at a user computing device the at least one actionable tremor mitigation action to a user, a healthcare provider, or both.

Apparatus and methods are described, including apparatus for use with a subject who shares a bed with a second person. A motion sensor detects motion of the subject and motion of the second person, and generates a motion signal in response thereto. A control unit identifies components of the motion signal that were generated in response to motion of the subject, by distinguishing between components of the motion signal that were generated in response to motion of the subject, and components of the motion signal that were generated in response to motion of the second person. The control unit analyzes the components of the motion signal that were generated in response to motion of the subject and generates an output in response thereto. Other applications are also described.