Abstract: A non-invasive, passive, and fully automated heart-sound-based system and method that provides estimates for blood velocity, tissue motion, and cardiac chamber size parameters for cardiac assessment is provided. The system uses a computer processor and software to receive PCG acoustic signals from one or more sensors and simultaneously receive electrocardiogram (ECG) signals from one or more sensors that are attached to a patient. The phonocardiogram (PCG) processing system and methods compute proxy metrics for echocardiographic parameters of cardiac tissue motion and valvular blood flow for evaluation.

Abstract: Systems and methods are provided for the non-invasive computation of Pulmonary Artery Pressure (and its components of mean, systolic and diastolic) (PAP) as well as Pulmonary Capillary Wedge Pressure (and its components of mean, A-Wave and V-Wave) (PCWP) using a wearable sensor device. Cardiac acoustic and electrocardiogram sensor signals are obtained and multiple temporal, amplitude-based, and spectral features are extracted from the signals. Extracted features from a subject are used as inputs for pre-trained classification, regression, or advanced machine learning models to provide an accurate computation of PAP and PCWP and their associated component values without surgery.

Abstract: An integrated cardio-respiratory system that fuses continuously recorded data from multiple physiological sensor sources to acquire signals representative of acoustic events caused by physiological phenomena occurring in the cardiac and/or arterial structures underneath particular areas of the chest and/or neck to monitor cardiac and respiratory conditions.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: A wireless remote monitoring system for biomedical fluid management that uses one or more sensors to detect changes in fluid or air in any container attached to a patient in order to monitor pre-surgical or post-surgical progress or complications. The sensors may be configured to monitor any type of container used to collect fluid or air from the human body, to transmit the sensor signals wirelessly to any number of devices including, but not limited to, cell phones or devices which in turn can send data to a functional repository where it can be analyzed and potentially acted upon by either a central or distributed network of providers.

Abstract: An exercise system comprising: an exercise cycle comprising a crank, wherein the crank is configured to be rotated by a user; and a sensing unit coupled to the exercise cycle, wherein the sensing unit is configured to sense information related to at least one crank measurement and to transmit the information related to the at least one crank measurement to a remote device via wireless communication. In some embodiments, the remote device is a mobile device. In some embodiments, the at least one crank measurement comprises a rotational speed of the crank or a force applied to the crank. The remote device can display information related to use of the exercise cycle based on the information received from the exercise cycle.

Abstract: Systems and methods are disclosed that use wireless coupling of energy for operation of both external and internal devices, including external sensor arrays and implantable devices. The signals conveyed may be electronic, optical, acoustic, biomechanical, and others to provide in situ sensing and monitoring of internal anatomies and implants using a wireless, biocompatible electromagnetic powered sensor systems.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: An integrated cardio-respiratory system that fuses continuously recorded data from multiple physiological sensor sources to acquire signals representative of acoustic events caused by physiological phenomena occurring in the cardiac and/or arterial structures underneath particular areas of the chest and/or neck to monitor cardiac and respiratory conditions.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: An exercise system comprising: an exercise cycle comprising a crank, wherein the crank is configured to be rotated by a user; and a sensing unit coupled to the exercise cycle, wherein the sensing unit is configured to sense information related to at least one crank measurement and to transmit the information related to the at least one crank measurement to a remote device via wireless communication. In some embodiments, the remote device is a mobile device. In some embodiments, the at least one crank measurement comprises a rotational speed of the crank or a force applied to the crank. The remote device can display information related to use of the exercise cycle based on the information received from the exercise cycle.

Abstract: A physiological monitoring device may comprise a sensor chamber defined by a flexible membrane on a first side and one or more walls and an exterior chamber surrounding the sensor chamber on all sides except for the first side. The sensor chamber may be airtight except for a pressure vent. The exterior chamber may comprise one or more exterior vents and may be arranged to allow for pressure escaping through the pressure vent to equilibrate the sensor chamber when the one or more exterior vents are blocked.

Abstract: An exercise system comprising: an exercise cycle comprising a crank, wherein the crank is configured to be rotated by a user; and a sensing unit coupled to the exercise cycle, wherein the sensing unit is configured to sense information related to at least one crank measurement and to transmit the information related to the at least one crank measurement to a remote device via wireless communication. In some embodiments, the remote device is a mobile device. In some embodiments, the at least one crank measurement comprises a rotational speed of the crank or a force applied to the crank. The remote device can display information related to use of the exercise cycle based on the information received from the exercise cycle.

Abstract: A remote patient monitoring system which is particularly well-suited for clinical out of center sleep testing (clinical OCST). Sensor nodes are grouped for wearing on the head, chest, and lower body of the patient. Sensor nodes wirelessly communicate sensor data to a nearby gateway, which communicates this data over a network (e.g., Internet) to one or more servers. The server(s) analyzes the sensor data for usage assurance, and to discern patient sleep patterns and characteristics. Guidance messages are generated by the system based on the analysis. Interfaces are provided to allow the patient to access their own information, and healthcare providers to access information collected regarding their patients.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: An exercise system comprising: an exercise cycle comprising a crank, wherein the crank is configured to be rotated by a user; and a sensing unit coupled to the exercise cycle, wherein the sensing unit is configured to sense information related to at least one crank measurement and to transmit the information related to the at least one crank measurement to a remote device via wireless communication. In some embodiments, the remote device is a mobile device. In some embodiments, the at least one crank measurement comprises a rotational speed of the crank or a force applied to the crank. The remote device can display information related to use of the exercise cycle based on the information received from the exercise cycle.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

Abstract: A method for determining the motive instability of an individual using foot pressure, foot speed and foot direction data collected from sensors on shoes. The sensed data is used to determine the minimum number and the placement of pressure sensors in the shoe. The data from the sensors is processed to extract spatial and temporal parameters as desired. The data is grouped into segments based on a segmentation rule. The trend in each segment is determined. The variability of the trend in each segment is determined. The risk of fall is computed on the basis of the trend and variance. The computation is adjustable by emphasizing certain parameters in order to tailor the instability assessment to a specific individual.

Abstract: The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.