An electromechanical device for rehabilitation includes pedals coupled to radially-adjustable couplings, an electric motor coupled to the pedals via the radially-adjustable couplings, and a control system including a processing device operatively coupled to the electric motor. The processing device configured to, responsive to a first trigger condition occurring, control the electric motor to operate in a passive mode by independently driving the radially-adjustable couplings rotationally coupled to the pedals. The processing device also configured to, responsive to a second trigger condition occurring, control the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the radially-adjustable couplings, and cause the electric motor to drive the radially-adjustable couplings when the measured revolutions per minute satisfy a threshold condition, and responsive to a third trigger condition occurring, control the electric motor to operate in a resistive mode by providing resistance to rotation of the radially-adjustable couplings.

An Internet of Thing (IoT) device includes a camera coupled to a processor; and a wireless transceiver coupled to the processor. Blockchain smart contracts can be used with the device to facilitate secure operation.

An athlete wearing footwear measures jump heights with a motion sensor mounted on the footwear over toes of the athlete. By sensing vertical jump start motions the sensor detects jump start and finish times of −4 g start and −4 g landing. The sensor, a body wearable mems sensor developed by JAWKU, L.L.C., has a previously installed generic factory scale calibration factor. The athlete replaces this calibration factor with a new calibration scale factor selecting an “absolute” external reference device which measures jump height. This device measures several jump heights then inputted to an algorithm app in the sensor to calculate the new calibration scale factor customized to the actual athlete. The motion sensor has built in programming apps to periodically receive an upgraded factory scale calibration factor which upgrade is based on an ever increasing data pool of jump heights. The updated factory calibration factor is then again replaced by the athlete personally taking several new measured jumps which jump heights are in turn inputted to the sensor. The progress made in evolving jumping skills based on training and specific conditioning exercises can thus be motion sensor evaluated.

An apparatus and method to evaluate the endurance of a muscle group of a user by measuring velocities of an engagement assembly coupled to a resistance element and moved by the user through a plurality of exercise strokes. A muscle group is assessed by determining a rate of fatigue for the muscle group. The method can include adjusting the controllable resistance to a resistance level, monitoring the movement of the engagement assembly against the resistance level over a plurality of repetitions, determining a relationship between the movement of the engagement assembly by the user over the plurality of repetitions, and using the relationship to determine the rate of fatigue.

The inventive apparatus comprises a mat member. The mat includes a scale physically secured to the mat member. The scale has an electrical output strives an electronic circuit. A display displays a weight measured by the scale. A switch is distributed over an area roughly commensurate with the area needed to trot in place. The switch is secured to the mat and underlies a top surface of the mat. The switch is coupled to the electronic circuit. The electronic circuit is programmed to count the number of times that the switch is closed, whereby trotting in place results in closing the switch in response to trotting paces. The above transducers are used to monitor and crosscheck activity and dietary information input into the system by the user and make recommendations.

Vital sign monitoring via remote sensing on stationary exercise equipment is provided. A new non-contact approach described herein uses radio frequency (RF) radar (e.g., ultra-wide band (UWB) radar) to remotely monitor vital sign information (such as heartbeat and breathing) and human activity information of subjects using stationary exercise equipment. In some embodiments, a radar sensor captures micro-scale chest motions (corresponding to the vital sign information) as well as macro-scale body motions (corresponding to movements from exercise). A signal processor receives radar signals from the radar sensor and processes the radar signals to reconstruct vital sign information from the micro-scale chest motions and/or human activity information from the macro-scale body motions using a joint vital sign-motion model, which can be trained using machine learning and other approaches.

An electromechanical device for rehabilitation includes pedals coupled to radially-adjustable couplings, an electric motor coupled to the pedals via the radially-adjustable couplings, and a control system including a processing device operatively coupled to the electric motor. The processing device configured to, responsive to a first trigger condition occurring, control the electric motor to operate in a passive mode by independently driving the radially-adjustable couplings rotationally coupled to the pedals. The processing device also configured to, responsive to a second trigger condition occurring, control the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the radially-adjustable couplings, and cause the electric motor to drive the radially-adjustable couplings when the measured revolutions per minute satisfy a threshold condition, and responsive to a third trigger condition occurring, control the electric motor to operate in a resistive mode by providing resistance to rotation of the radially-adjustable couplings.

This application relates to the field of electronic technologies, and provides an action recognition method and apparatus, a terminal device, and a motion monitoring system. Characteristic extraction and action recognition are performed based on motion data collected by data collection apparatuses; a gait characteristic, a swing gesture characteristic, and an image action characteristic of a user are recognized by using a plurality of pieces of motion data; and a type of a hitting action of a player is determined based on the gait characteristic, the swing gesture characteristic, and the image action characteristic. In this way, the hitting action of the player in a motion process can be accurately recognized. This is conducive to performing comprehensive analysis on a comprehensive sports capability of the player, and is more helpful to formulating a personalized training plan for the player.

Hand held weight units of light weight manufactured as a solid unit, a shell unit with core insert combinations or modular units with interlocking ends. Shell units with core inserts and modular interlocking units allow for the changing of held weight by inserting or removing inserts or by locking or unlocking of modular weight unit sets creating varying held weight. The weight units are primarily used with upper body exercises during aerobic exercises in the home, outdoors, or in a gym setting such as walking or running to vary the intensity of workout during use.

Systems and methods for sports analytics are disclosed. A system for evaluating athletic performance can include wearable sensor devices configured to be removably coupled to an athlete's body during athletic performance, and one or more equipment-mountable sensor devices configured to be coupled to reference objects adjacent to an athletic performance site such as a goal or equipment. A computing device can be communicatively coupled to the wearable sensor device(s) and the equipment-mountable sensor device(s). The computing device is configured to receive sensor data from each of the sensor device(s) and to determine at least one performance parameter.