Methods and systems for close proximity monitoring of subjects, including in particular at-risk psychiatric subjects, where subjects have a tamper-resistant wearable device that signals an observer device, an observer records an observation of the subject, and the observer device emits an alert when the observer fails to record an observation during a predetermined time period.

A method includes: obtaining, at a server, a diagnosis key published by a tracing service, the diagnosis key associated with a source device; identifying one or more detected proximity identifiers associated with the diagnosis key; identifying one or more affected network detection devices associated with the one or more detected proximity identifiers; retrieving, from a repository, device data for the one or more affected network devices, the device data identifying a facility of the one or more affected network devices; and generating facility exposure data for the facility, the facility exposure data defining exposure associated with the source device at the facility.

A prevention and safety management system utilizes a non-intrusive imaging sensor (e.g. surveillance cameras, smartphone cameras) and a computer vision system to record videos of workers not wearing sensors. The videos are analyzed using a deep machine learning algorithm to detect kinematic activities (set of predetermined body joint positions and angles) of the workers and recognizing various physical activities (walk/posture, lift, push, pull, reach, force, repetition, duration etc.). The measured kinematic variables are then parsed into metrics relevant to workplace ergonomics, such as number of repetitions, total distance travelled, range of motion, and the proportion of time in different posture categories. The information gathered by this system is fed into an ergonomic assessment system and is used to automatically populate exposure assessment tools and create risk assessments.

Methods and systems for monitoring workplace safety and evaluating risks is provided, the method comprising receiving signals from at least one wearable device, identifying portions of the signals corresponding to physical activities, excerpting the portions of the signals corresponding to the physical activities, and calculating risk metrics based on measurements extracted from the excerpted portions of the signals, the risk metric indicative of high risk lifting activities.

The present disclosure relates to a concept for providing a score indicative of a physiological condition of at least one individual. The concept include measuring physiological or behavioral data of the individual, determining contextual data comprising a measurement context and/or socio-demographic features associated with the individual determining a metric based on the measured physiological or behavioral data, and determining the score by comparing the metric against a predicted value from a prediction model trained with the contextual data.

A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.

Systems and methods for monitoring physiological parameter(s) and environmental condition(s) of a subject include an in-ear wearable apparatus. The in-ear wearable apparatus includes a housing, a controller coupled to the housing, a first plurality of physiological sensors coupled to the housing and configured to detect a plurality of physiological parameters, and at least one environmental sensor coupled to the housing and configured to detect at least one environmental condition.

Methods and systems for monitoring workplace safety and evaluating risks is provided, the method comprising receiving signals from at least one wearable device, identifying portions of the signals corresponding to physical activities, excerpting the portions of the signals corresponding to the physical activities, and calculating risk metrics based on measurements extracted from the excerpted portions of the signals, the risk metric indicative of high risk lifting activities.

A contextualized sensor system is provided comprising one or more sensors, one or more memory elements, a library of alert rules stored in the one or more memory elements, one or more processors, and the one or more memory elements including instructions that, when executed, cause the one or more processors to perform operations comprising: receiving from one of the one or more sensors one or more sensor data, comparing the first sensor data to a library of alert rules to determine whether an alert situation has occurred, and communicating an alert if the alert situation has occurred. In some embodiments, the operations further comprise contextualizing an environmental data, a location data, a physiological data, a behavior data and an orientation data.

This system is directed to the management of insurance information, risks and coverage, wherein a set of non-transitory computer readable instructions included in a kiosk disposed at the construction site can include instructions for creating a certificate of insurance according to a determination that the insurance requirements have been met and storing the certificate of insurance in the distributed ledger.