An integrated method for the dynamic control of the speed of a treadmill having a belt that turns about two rollers; a motor that sets said belt in action; and comprises the following steps: determining the position of the centre of mass (x COM) of a user who moves on said belt by means of a 3D video camera which consists of an RGB camera and an infrared ray depth sensor and is able to shoot said user entirely; determining the difference (x dif) between said position of the centre of mass (x COM) measured and a pre-set reference position (x ref); determining the anteroposterior horizontal force (F AP) of a user who moves on said belt; computing a weighted combination between said difference (x dif) and said anteroposterior horizontal force (F AP), using a data-processing unit, to obtain a third speed; applying said third speed to said belt by means of said motor; wherein the step of computing a weighted combination comprises the step of applying said difference (x dif) and said anteroposterior horizontal force (F AP) to a Kalman filter, represented by the generic formula { x ̇ t = Ax t + Bu t + rx t z t = Cx t + ry t where: x(t) is the state of the system; A, B, and C are, respectively, the state-transition matrix, the input-control matrix, and the measurement-observation matrix; u(t) is the control input vector and z(t) is the measurement input vector; and rx(t) and ry(t) are, respectively, the measurement noise and the process noise. having a belt that turns about two rollers; a motor that sets said belt in action; and comprises the following steps: determining the position of the centre of mass (x COM) of a user who moves on said belt by means of a 3D video camera which consists of an RGB camera and an infrared ray depth sensor and is able to shoot said user entirely; determining the difference (x dif) between said position of the centre of mass (x COM) measured and a pre-set reference position (x ref); determining the anteroposterior horizontal force (F AP) of a user who moves on said belt; computing a weighted combination between said difference (x dif) and said anteroposterior horizontal force (F AP), using a data-processing unit, to obtain a third speed; applying said third speed to said belt by means of said motor; wherein the step of computing a weighted combination comprises the step of applying said difference (x dif) and said anteroposterior horizontal force (F AP) to a Kalman filter, represented by the generic formula { x ̇ t = Ax t + Bu t + rx t z t = Cx t + ry t where: x(t) is the state of the system; A, B, and C are, respectively, the state-transition matrix, the input-control matrix, and the measurement-observation matrix; u(t) is the control input vector and z(t) is the measurement input vector; and rx(t) and ry(t) are, respectively, the measurement noise and the process noise. determining the position of the centre of mass (x COM) of a user who moves on said belt by means of a 3D video camera which consists of an RGB camera and an infrared ray depth sensor and is able to shoot said user entirely; determining the difference (x dif) between said position of the centre of mass (x COM) measured and a pre-set reference position (x ref); determining the anteroposterior horizontal force (F AP) of a user who moves on said belt; computing a weighted combination between said difference (x dif) and said anteroposterior horizontal force (F AP), using a data-processing unit, to obtain a third speed; applying said third speed to said belt by means of said motor; wherein the step of computing a weighted combination comprises the step of applying said difference (x dif) and said anteroposterior horizontal force (F AP) to a Kalman filter, represented by the generic formula { x ̇ t = Ax t + Bu t + rx t z t = Cx t + ry t where: x(t) is the state of the system; A, B, and C are, respectively, the state-transition matrix, the input-control matrix, and the measurement-observation matrix; u(t) is the control input vector and z(t) is the measurement input vector; and rx(t) and ry(t) are, respectively, the measurement noise and the process noise. x(t) is the state of the system; A, B, and C are, respectively, the state-transition matrix, the input-control matrix, and the measurement-observation matrix; u(t) is the control input vector and z(t) is the measurement input vector; and rx(t) and ry(t) are, respectively, the measurement noise and the process noise.