Virtual Proprioception with Eccentric Training for a Shoulder Press by a Wearable System

The confluence of wearable systems with machine learning enables the opportunity for quantified exercise with the ability to discern specifically personalized strategies, such as eccentric strength training. Using a unique software application with a smartphone to provide real-time feedback with respect to the gyroscope signal, effective threshold bounds can be prescribed and maintained during an exercise, such as a shoulder press. The concept of utilizing visualized feedback from an inertial sensor, such as a gyroscope, for regulating human movement is known as Virtual Proprioception. Additionally, the smartphone software application is capable of recording the gyroscope signal for wireless transmission to an email account, which constitutes a provisional Cloud computing environment. Given these characteristics, the smartphone has the functional properties of a wearable and wireless gyroscope platform. Post-processing of the gyroscope signal data for an eccentric training oriented shoulder press using the smartphone by means of Virtual Proprioception providing real-time feedback can be differentiated relative to standard strength training through a machine learning algorithm, such as a multilayer perceptron neural network, and considerable classification accuracy has been attained. The implications are augmented acuity for strength training strategies that are highly specified according to personalized exercise objectives.