World data is established, including real-world position and/or real-world motion of an entity. Target data is established, including planned or ideal position and/or motion for the entity. Guide data is established, including information for guiding a person or other subject in bringing world data into match with target data. The guide data is outputted to the subject as virtual and/or augmented reality data. Evaluation data may be established, including a comparison of world data with target data. World data, target data, guide data, and/or evaluation data may be dynamically updated. Subjects may be instructed in positions and motions by using guide data to bring world data into match with target data, and by receiving evaluation data. Instruction includes physical therapy, sports, recreation, medical treatment, fabrication, diagnostics, repair of mechanical systems, etc.

A method uses telemetry data based on user habit information or user monitoring. User information is acquired from one or more sensors of a monitoring device. The user information is selected from of at least one of, a user's activities, behaviors and habit information. Signals are routed through ID circuitry at the user monitoring device. User information is communicated between the monitoring device and a telemetry system. A database of user ID's is accessed at the telemetry system. The telemetry system analyzes telemetry data based on at least one of, user's activities, behaviors and habit information, user condition, and user parameter, to create personalized information about the user. One or more contexts of a user activity are associated with an activity manager. The activity manager is a standalone device, included with the telemetry system or included with the monitoring device.

A malware detection system and method detects changes in host behavior indicative of malware execution. The system uses linear discriminant analysis (LDA) for feature extraction, multi-channel change-point detection algorithms to infer malware execution, and a data fusion center (DFC) to combine local decisions into a host-wide diagnosis. The malware detection system includes sensors that monitor the status of a host computer being monitored for malware, a feature extractor that extracts data from the sensors corresponding to predetermined features, local detectors that perform malware detection on each stream of feature data from the feature extractor independently, and a data fusion center that uses the decisions from the local detectors to infer whether the host computer is infected by malware.

Systems and methods for improving vehicle wrong-way detection including systems, sensors or features, such as those for system/network clocks, ambient light, driver impairment, automatic cruise control, blind spot warning or configurable vehicle security, either singly or in any combination therewith, may refine and/or modify the confidence level of detecting particular or potential wrong-way incidents. By modifying the confidence level, the system may change the system sensitivity by moving the cutoff point at which an alert activation takes place. The systems described above can modify the wrong-way detection sensitivity. Greater confidence, or system sensitivity, may also help enable the system to take further actions beyond a simple warning. At greater confidence levels, the system could intervene, by way of a non-limiting example, with throttle limiting, turning on the hazard lights or even bringing the vehicle to a stop.

The present disclosure includes a retrofit sensor module for use with a protective head top with a helmet and a visor. The sensor module includes a sensor housing and an attachment mechanism. The sensor housing encloses a head presence sensor to sense when the protective head top is being worn. The sensor housing also encloses a position sensor to sense the position of the visor relative to the helmet. The retrofit sensor module further comprises an attachment mechanism secured to the housing. The attachment mechanism mates with a first hinge component of a hinge assembly in the protective head top to removably install the sensor module into the protective head top, wherein the hinge assembly allows the visor to move relative to the helmet.

Apparatus for managing animals such as but not limited to livestock. In some embodiments, an ear tag assembly has a tag, a backing member and an elongated shaft assembly configured to pierce and extend through an outer ear (auricle) of the animal. A first temperature sensor is disposed within the shaft assembly to obtain an ear temperature measurement of the animal. A second temperature sensor obtains an ambient temperature measurement adjacent the animal. A control circuit accumulates temperature data from the first and second temperature sensors in a tag memory for subsequent transfer, via a wireless communication network, to a data collection unit. Additional sensors may be utilized. In some cases, the backing member may house a battery to power the control circuit. The backing member may be removably attachable to the shaft to allow replacement of the battery.

An alarm device adapted for use with a self-retracting lanyard having a main body and a retractable line coupled to the main body. The alarm device includes a housing adapted to be engageable with the retractable line. The housing defines a detection axis, with the housing being adapted to allow at least a portion of the retractable line to be parallel to the detection axis when the housing is engaged with the retractable line. An inclinometer is coupled to the housing and is adapted to detect a magnitude of an angle between the detection axis and a vertical axis, and generate an electrical signal when the magnitude exceeds a preset threshold. An alarm element is in communication with the inclinometer to receive the electrical signal generated by the inclinometer. The alarm element is adapted to generate an alert signal in response to receipt of the electrical signal.

Systems and methods for monitoring elevations of a set of workers at a job site include reference devices for capturing barometric pressures at different installation elevations on a structure and portable barometric pressure sensors carried by the workers. An atmospheric pressure offset may be calculated using the reference barometric pressures and installation elevations. The atmospheric pressure offset may be applied to atmospheric pressure measurements captured by the barometric pressure sensors carried by the workers to determine worker elevations. The worker elevations may be converted to corresponding floors of the structure.

A travel controller performs driving control of a vehicle at an autonomous driving level that is a first autonomous driving level in which a driver of the vehicle is not obligated to watch or a second autonomous driving level in which the driver is obligated to watch; notifies, for a predetermined period, the driver of a request for driving the vehicle or taking a driving posture allowing for the driving, when the autonomous driving level of the driving control changes from the first autonomous driving level to the second autonomous driving level; and determines an attention level of the driver, based on an output of a sensor provided for the vehicle. The attention level is a level of attention to driving. The travel controller further modifies the predetermined period so as to be shorter when the determined attention level is high than when the attention level is low.

A system includes video cameras arranged to monitor a vulnerable person, and a processor system that receives video frames from the video cameras, the processor system comprising a processor and a non-transitory, computer-readable storage medium having machine instructions executed by the processor. The processor detects and identifies objects in a current received video frame, classifies an identified object as the person by applying a facial recognition algorithm that identifies the person, determines a posture of the person by identifying joints, limbs, and body parts, and their respective orientations to each other and to a plane, and immediately discards the current video frame. The processor then determines a change in motion, of the person, between the current received video frame and one or more prior received video frames, and, based on the determined posture and the change in motion, determines that the person has experienced a defined event. An example method for monitoring health and well-being of persons includes a processor integral to a video camera capturing video frames containing raw image data of a scene containing person objects and non-person objects. The processor, for a first video frame, executes video frame operations, including detecting the person objects in the first video frame, detecting and identifying the non-person objects in the first video frame, identifying and classifying a person object as a person for metric analysis, and determining a posture of the person. The method further includes the processor repeating the video frame operations for second and subsequent video frames; identifying changes in posture of the person from the second and subsequent video frames; using the changes in posture, determining motion of the person; providing person privacy, comprising automatically and immediately following each video frame operation on a video frame, deleting all raw image data of the video frame; and, based on the posture changes and the motion, generating a private, personalized metric analysis for the person. In an aspect, the private, personalized metric analysis includes outcome measures of interest selected from a group consisting of steps taken/distance walked/changes in gait by elderly persons, positional changes for bedridden persons, and developmental milestones in child persons; and the processor conducts the metric analysis over time to identify trends and corresponding changes in a health condition of the person, comprising progress in rehabilitation, deterioration from a degenerative disease, and physical and mental development.