Intelligent gloves, method and system for calorie consumption and hand posture recognition
A technology of calorie consumption and hand posture, applied in gloves, clothing, sports accessories, etc., can solve the problem that the measurement module is not movable, cannot be truly wearable, and the module cannot be widely used.
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Embodiment 1
[0053] Such as figure 1 As shown, this example provides a smart glove for calorie consumption and hand gesture recognition, including: a processor module, a storage module, a communication module, a perception module, an early warning module, a display module, a power module, a switch module and a cloud server, The storage module, the communication module, the perception module, the warning prompt module, the display module, the power supply module, the switch module and the cloud server are respectively connected to the processor module, wherein the perception module includes a pressure sensor and an IMU inertial unit, and the The pressure sensor and the IMU inertial unit are respectively responsible for sensing the amount of pressure on the hand of the user during the fitness process and the IMU data, and then collect source data for the user's strength training, and transmit the source data to the processor module; the processing The processor module is responsible for prep...
Embodiment 2
[0057] Such as figure 2 and image 3 As shown, this example also provides a method for calorie consumption and hand gesture recognition, using the smart glove described in Embodiment 1, and including the following steps:
[0058] Step S1, using the thin-film pressure sensor of the pressure sensor and the IMU inertial unit to collect source data during strength training;
[0059] Step S2, transmitting the source data to the processor module for processing;
[0060] In step S3, the data obtained by the received pressure sensor and IMU inertial unit is subjected to an ETL process for ETL analysis. The ETL process is to extract, transpose, load and deliver the data obtained in step S2 through ETL technology process;
[0061] And, step S4, calculate the calories consumed, establish a classification model through the support vector machine, use the source data exceeding the strength training as the target action class, and others as the non-target action class; then judge whethe...
Embodiment 3
[0083] Such as Figure 4 As shown, this example also provides a system for calorie consumption and hand gesture recognition, which adopts the method for calorie consumption and hand gesture recognition described in Embodiment 2, and includes the following modules:
[0084] The signal acquisition and calculation module 41 is used to collect the signal when the smart glove is in motion, and evaluate the motion state information for preliminary calculation;
[0085] Anomaly detection module 42, used to identify whether the signal changes abnormally through an anomaly detection algorithm;
[0086] Action judging module 43, a type of support vector machine for distinguishing the target action class from other action classes, taking the abnormal pattern caused by exceeding the strength training threshold as the target action class, and judging whether the movement posture of the smart glove has a posture deviation ;
[0087] And, the alarm module 44 is used for sending out a warni...
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