Abstract:
Intelligent motion capture element that includes sensor personalities that optimize the sensor for specific movements and/or pieces of equipment and/or clothing and may be retrofitted onto existing equipment or interchanged therebetween and automatically detected for example to switch personalities. May be used for low power applications and accurate data capture for use in healthcare compliance, sporting, gaming, military, virtual reality, industrial, retail loss tracking, security, baby and elderly monitoring and other applications for example obtained from a motion capture element and relayed to a database via a mobile phone. System obtains data from motion capture elements, analyzes data and stores data in database for use in these applications and/or data mining. Enables unique displays associated with the user, such as 3D overlays onto images of the user to visually depict the captured motion data. Enables performance related equipment fitting and purchase. Includes active and passive identifier capabilities.
Abstract:
A method to determine a body pose of a user in a virtual reality or augmented reality system includes acquiring sensor data from a plurality of sensors in a garment worn by a user. The sensor data is processed to generate a processed sensor data set, wherein the processed sensor data set is scaled for the size of the user. The processed sensor data set is converted to a pose data set. The pose vector data set is then used by a viewer device to render the body pose of the user.
Abstract:
Provided is a pressure sensing element configured to be flexible, and capable of demonstrating a stable electrical reliability over a long period; and, a pressure sensor having such pressure sensing element. A pressure sensing element 100 has an electro-conductive pressure sensing film 14, a sensor electrode 12 provided at a position faced to the pressure sensing film 14, and an insulating layer 13 which creates a predetermined distance "A" between the pressure sensing film 14 and the sensor electrode 12 so as to keep them apart from each other, the pressure sensing film 14 being a resin film containing carbon particles 140; and, a pressure sensor 200 has the pressure sensing element 100, and a detection unit 210 which is electrically connected with the pressure sensing element 100 so as to detect contact resistance between the pressure sensing film 14 and the sensor electrode 12.
Abstract:
A furniture system includes a chair having a seat, a backrest coupled to the seat, and a base supporting the backrest and the seat. The furniture system also includes a plurality of sensors and a processor. Each sensor is operable to detect a physical force imparted by a user on the chair, and generate an output signal indicative of the physical force. The processor is coupled to the plurality of sensors, and is operable to receive the output signals generated by the plurality of sensors, and determine, based on at least one of the output signals, a current posture of the user sitting in the chair.
Abstract:
A person support apparatus, such as a bed, cot, stretcher, or the like, includes an exit detection system that utilizes an occupant motion parameter to determine whether to issue an alert or not. The motion parameter may be based on the weight and motion of the occupant. Successive positions of the occupant are determined in order to calculate a velocity of the occupant. In some embodiments, the kinetic energy of the occupant is used to determine if an alert should be issued. Objects positioned on the person support apparatus may also be detected and tracked. Auto-zeroing of a built-in scale, as well as automatic recognition of the removal, movement, and/or addition of objects is also provided.
Abstract:
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.
Abstract:
A patient support apparatus includes a load frame, a support frame, and a plurality of load cells supporting the load frame on the support frame such that a load supported by the load frame is supported by the load cells, each load cell configured to produce a signal indicative of a load weight bearing upon that load cell. The load cells are calibrated after installation.
Abstract:
Force or pressure transducer arrays have elastically stretchable electrically conductive polymer threads disposed in parallel rows and columns that contact at intersections thereof a piezoresistive material which has an electrical resistivity which varies inversely with pressure or force exerted thereon to form a matrix array of force or pressure sensor elements. The threads are fixed to a single one or pair of flexible elastically stretchable substrate sheets made of thin sheets of an insulating polymer such as PVC, or for greater elasticity and conformability to irregularly-shaped objects such as human body parts, an elastically stretchable fabric such as LYCRA or SPANDEX. Elastic stretchability of the sensor arrays is optionally enhanced by disposing either or both row and column conductive threads in sinuously curved, serpentine paths rather than straight lines.