Abstract: The system receives a visual representation of a scene, and without isolating an individual in the visual representation, provides the visual representation to an image feature extraction component. The system obtains from the image feature extraction component an image embedding vector representing the visual representation without isolating a single individual. The system obtains from a database a second whole-image embedding representation associated with a unique user identifier representing a user. The system determines whether the first whole-image embedding representation matches the second whole-image embedding representation. Upon determining that the first whole-image embedding representation matches the second whole-image embedding representation, the system generates an indication that the user is included in the visual representation.

Abstract: The system receives data indicating an individual and processes the data to isolate the individual and to enhance data quality. The system extracts a first multiplicity of key features of the data, which tend to uniquely identify the individual. The system compares, using artificial intelligence, the first multiplicity of key features associated with the data to a second multiplicity of key features associated with a user to determine whether the data indicates the user. Upon determining that the data indicates the user, the system retrieves from a datastore a rule associated with the second multiplicity of key features and determines whether the rule permits use of the data indicating the individual. Upon determining that the rule associated with the second multiplicity of key features does not permit use of the data indicating the individual, the system sends an indication that the rule does not permit the use.

Abstract: The system receives data indicating an individual and processes the data to isolate the individual and to enhance data quality. The system extracts a first multiplicity of key features of the data, which tend to uniquely identify the individual. The system compares, using artificial intelligence, the first multiplicity of key features associated with the data to a second multiplicity of key features associated with a user to determine whether the data indicates the user. Upon determining that the data indicates the user, the system retrieves from a datastore a rule associated with the second multiplicity of key features and determines whether the rule permits use of the data indicating the individual. Upon determining that the rule associated with the second multiplicity of key features does not permit use of the data indicating the individual, the system sends an indication that the rule does not permit the use.

Abstract: The system obtains a first image including a first individual and an indication that the first individual can be identified in the first image. Upon obtaining the indication, the system processes the first image using a first component and obtains a first identity embedding. The system obtains a second image including a second individual and other objects without obtaining an indication that the second individual can be identified. Upon obtaining the indication, the system provides the second image to a second component and obtains a first WIER representing the second image without isolating the second individual. The system provides the first identity embedding to a third component configured to convert the first identity embedding into a second WIER. The system determines whether the second image includes the first individual by determining whether the first and the second WIER match.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from a plurality of pressure sensing elements of a shoe sensor system, where the plurality of pressure sensing elements are distributed in a pattern having a first pressure sensing element at a fifth metatarsal area of a lateral wall of an insole of a shoe associated with the shoe sensor system, and a second pressure sensing element at a first metatarsal area of a medial wall of the insole. The method further includes processing the foot force data to determine a horizontal force of a foot of a user of the shoe sensor system while the user is performing a movement while wearing the shoe.

Abstract: An apparatus includes left and right shoe sensor systems. A shoe sensor system includes pressure sensing elements, an accelerometer, and a control circuit that includes a power source circuit, a clock circuit, a processing module, memory, a wireless communication transceiver, sensor communication links, and an accelerometer communication link. The processing module samples data from the pressure sensing element to produce foot force data and samples data from the first accelerometer to produce three-dimensional foot data. The wireless communication transmits as outbound radio frequency (RF) signals regarding the foot force data and the three-dimensional foot data.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from a plurality of pressure sensing elements of a shoe sensor system, where the plurality of pressure sensing elements are distributed in a pattern having a first pressure sensing element at a fifth metatarsal area of a lateral wall of an insole of a shoe associated with the shoe sensor system, and a second pressure sensing element at a first metatarsal area of a medial wall of the insole. The method further includes processing the foot force data to determine a horizontal force of a foot of a user of the shoe sensor system while the user is performing a movement while wearing the shoe.

Abstract: A method includes collecting, for a pitch, per pitch data that includes a plurality of first arm orientation data points and a plurality of second arm orientation data points. The method further includes analyzing the per pitch data to determine a release point arm orientation and an effort level. The method further includes calculating a per pitch stress level based on the release point arm orientation and the effort level. The method further includes calculating, for a set of pitches, a fatigue level based on the per pitch stress level of each pitch of the set of pitches.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

Abstract: A method includes collecting, for a pitch, per pitch data that includes a plurality of first arm orientation data points and a plurality of second arm orientation data points. The method further includes analyzing the per pitch data to determine a release point arm orientation and an effort level. The method further includes calculating a per pitch stress level based on the release point arm orientation and the effort level. The method further includes calculating, for a set of pitches, a fatigue level based on the per pitch stress level of each pitch of the set of pitches.

Abstract: A method includes transmitting one or more local transmit radar signals and receiving a plurality of local receive radar signals that corresponds to the one or more local transmit radar signals being reflected or refracted off an object. The method further includes processing the plurality of local receive radar signals to determine a relative position of the object. The method further includes determining whether the object is positioned within a desired detection volume associated with a position of the transmitter section. When the object is positioned within the desired detection volume, further processing the local receive radar signals to determine movement pattern of the object and whether the movement pattern of the object is within a range of plausible movements for the object. When the movement pattern of the object is within a range of plausible movements for the object, tracking movement of the object within the desired detection volume.

Abstract: A method includes collecting, for a pitch, per pitch data that includes a plurality of first arm orientation data points and a plurality of second arm orientation data points. The method further includes analyzing the per pitch data to determine a release point arm orientation and an effort level. The method further includes calculating a per pitch stress level based on the release point arm orientation and the effort level. The method further includes calculating, for a set of pitches, a fatigue level based on the per pitch stress level of each pitch of the set of pitches.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

Abstract: A wireless in-shoe physical activity monitoring apparatus includes left and right shoe sensor systems. A shoe sensor system includes pressure sensing elements, an accelerometer, and a control circuit that includes a power source circuit, a clock circuit, a processing module, memory, a wireless communication transceiver, sensor communication links, and an accelerometer communication link. The processing module samples data from the pressure sensing element to produce foot force data and samples data from the first accelerometer to produce three-dimensional foot data. The wireless communication transmits as outbound radio frequency (RF) signals regarding the foot force data and the three-dimensional foot data.

Abstract: A method includes transmitting one or more local transmit radar signals and receiving a plurality of local receive radar signals that corresponds to the one or more local transmit radar signals being reflected or refracted off an object. The method further includes processing the plurality of local receive radar signals to determine a relative position of the object. The method further includes determining whether the object is positioned within a desired detection volume associated with a position of the transmitter section. When the object is positioned within the desired detection volume, further processing the local receive radar signals to determine movement pattern of the object and whether the movement pattern of the object is within a range of plausible movements for the object. When the movement pattern of the object is within a range of plausible movements for the object, tracking movement of the object within the desired detection volume.

Abstract: A wireless in-shoe physical activity monitoring apparatus includes left and right shoe sensor systems. A shoe sensor system includes pressure sensing elements, an accelerometer, and a dongle that attaches to an exterior of a shoe. The includes a power source circuit, a clock circuit, a processing module, memory, wireless communication transceiver, sensor communication links, and an accelerometer communication link. The processing module samples, in accordance with a sampling signal, data from the plurality of pressure sensing element to produce foot force data and samples, in accordance with the sampling signal, data from the first accelerometer to produce three-dimensional foot data. The wireless communication transceiver transmits as outbound radio frequency (RF) signals regarding the foot force data and the foot three-dimensional data.

Abstract: A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

Abstract: A method includes collecting, for a pitch, per pitch data that includes a plurality of first arm orientation data points and a plurality of second arm orientation data points. The method further includes analyzing the per pitch data to determine a release point arm orientation and an effort level. The method further includes calculating a per pitch stress level based on the release point arm orientation and the effort level. The method further includes calculating, for a set of pitches, a fatigue level based on the per pitch stress level of each pitch of the set of pitches.

Abstract: An adjustable workpiece rotating assembly for rotating a workpiece of any of a plurality of configurations at a workstation positioned along a conveyor system. Workpiece carriers attached to the conveyor system allow the workpiece to be positioned thereupon at a desired orientation relative to the work station. A mouth piece and base cup engage opposite ends of the workpiece and cause the workpiece to be rotated about a longitudinal axis thereof. The mouth piece and base cup are supported by a vertical displaceable rack which engage with a rotatably spur gear to allow the mouth piece and base cup to be translated simultaneously in the vertical direction to engage with a workpiece of any one of different elevations.