Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems

Abstract

Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR) digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases.

Analysis of the pushing phase in Paralympic cross-country sit-skiers – Class LW10

“…Furthermore, in a non-racing contest or in laboratory tests more accurate kinematic measurements can be obtained using technologies that are already well-assessed in clinical environment e.g. electrogoniometers [27], [28], inertial sensors [29] or marker stereophotogrammetric analysis [30].…”

Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications

“…The front-end component of wearable systems is a wide variety of wearable sensors, which can be used to measure all kinds of physiological parameters, such as body temperature [10,11], myoelectricity [12], heart rate [13], and blood glucose [14]. They are also used for human motion detection, such as acceleration [15], muscle ductility [6,16], and foot pressure [17,18]; as well as environment monitoring, for example, position coordinates [19], temperature [20,21], humidity [22,23], and atmospheric pressure [24]. Different from other functional wearable sensors, strain sensors are attached to the joint or even directly mounted on the muscle for human motion inspection [25][26][27][28][29].…”

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

“…This double direct integration can result in an accumulation of drift error giving wrong velocity, wrong position, and wrong distance and therefore wrong step length. In the literature, there are solutions to solve the drift error so that it is possible to integrate twice the raw acceleration data and to measure distance [9,15]. By integration, the typical percentages of error over the walking distance are between 2.5% and 5.0% [16].…”