System and method for multi-sensor combination for indirect sport assessment and classification

Abstract

A system for measuring power output of a runner is disclosed. In some embodiments the system comprises a first sensor component including a first sensor, microprocessor, and a signal transceiver; a second sensor component including a second sensor and a signal transmitter; wherein the first sensor is configured to measure a vertical velocity and horizontal velocity, the second sensor is configured to measure the slope angle of a foot of the runner during a stance phase of the foot, the signal transmitter configured to send slope angle data, the signal transceiver configured to receive the slope angle data from the signal transmitter, and the microprocessor has computing instructions configured to calculate a power output based on the vertical velocity, horizontal velocity, and slope angle data.

What is claimed is: 
     
         1 . A method for measuring a power output of a runner, comprising: attaching a single sensor at a single sensor position on the runner; detecting slope changes of sensor data from said single sensor; and retroactively applying said slope changes to spatio-temporal data to measure the power output; wherein said single sensor comprises an inertial measurement unit (IMU). 
     
     
         2 . The method of  claim 1 , wherein said single sensor position is selected from the group consisting of a foot, a wrist or a head of the runner. 
     
     
         3 . The method of  claim 1 , wherein said spatio-temporal data comprises data relating to ground contact time, flight phase duration, swing phase duration, and cadence. 
     
     
         4 . The method of  claim 3 , wherein foot pronation angle and foot strike angle are calculated according to said sensor data. 
     
     
         5 . The method of  claim 1 , wherein the power output is calculated according to both initial contact and terminal contact events in footfall of the runner. 
     
     
         6 . The method of  claim 5 , further comprising performing an initialization method to remove an influence of an orientation of the sensor on accuracy of the power output calculation, wherein said initialization method comprises detecting initial contact and terminal contact of the foot with the ground, and determining said orientation of the sensor before power output is calculated. 
     
     
         7 . The method of  claim 1 , wherein said sensor comprises a sensor assembly, wherein said sensor assembly comprises a single IMU, a microcontroller, and a wireless communications device to transmit sensor data externally. 
     
     
         8 . The method of  claim 1 , further comprising analyzing said sensor data from said single sensor by a machine learning algorithm; wherein said machine learning algorithm is trained according to a combination of data from a force plate on a shoe worn by the runner and said sensor data from said single sensor. 
     
     
         9 . The method of  claim 8 , wherein said single sensor or a separate wearable device worn by the runner comprises a self-learning power meter, wherein said power meter comprises a microprocessor and an embedded machine learning library, wherein said machine learning algorithm is executed by said power meter for analyzing said sensor data from said single sensor. 
     
     
         10 . The method of  claim 9 , wherein said machine learning algorithm is selected from the group consisting of an LSTM (long short-term memory) network; an RNN (recurrent neural network); a CNN (convoluted neural network); and an MNN (modular neural network). 
     
     
         11 . A system for measuring the power output of a runner, comprising:
 a sensor configured to detect slope changes and attached to or worn by the runner;   and a computing device having a processor and a memory for storing buffered data from said sensor for a predetermined number of sampling cycles and having stored thereon instructions for execution by a processor to cause the computational device to receive data sampled from said sensor; during an activity of the user, to retroactively apply slope measurement to spatio-temporal data; and to estimate a power output based in part on the slope trajectory.   
     
     
         12 . The system of  claim 11 , wherein said sensor comprises a sensor assembly, wherein said sensor assembly comprises a single IMU, a microcontroller, and a wireless communications device to transmit sensor data externally. 
     
     
         13 . The system of  claim 12 , wherein said single sensor position is selected from the group consisting of a foot, a wrist or a head of the runner. 
     
     
         14 . The system of  claim 13 , wherein said sensor is integrated as part of a shoe. 
     
     
         15 . The system of  claim 14 , further comprising a machine learning algorithm; wherein said machine learning algorithm is trained according to a combination of data from a force plate on a shoe worn by the runner and said sensor data from said single sensor; wherein said machine learning algorithm analyzes said sensor to estimate said power output. 
     
     
         16 . The system of  claim 11 , wherein said single sensor comprises a self-learning power meter, wherein said power meter comprises a microprocessor and an embedded machine learning library, wherein said machine learning algorithm is executed by said power meter for analyzing said sensor data from said single sensor. 
     
     
         17 . The system of  claim 16 , wherein said machine learning algorithm is selected from the group consisting of an LSTM (long short-term memory) network; an RNN (recurrent neural network); a CNN (convoluted neural network); and an MNN (modular neural network). 
     
     
         18 . The system of  claim 11 , further comprising a separate wearable device worn by the runner, said separate wearable device comprising a self-learning power meter, wherein said power meter comprises a microprocessor and an embedded machine learning library, wherein said machine learning algorithm is executed by said power meter for analyzing said sensor data from said single sensor.