Use accelerometer data to predict human gait cycles.

Gait cycle segmentation is not only useful for wearable products, but also for applicatiosn in sports, rehabilitation, and clinical diagnosis. For example, automated selection of the gait parameters is very helpful for medical practices to monitor patients's healing progress and help physician's decision making. Therefore, gait analysis has attracted the interest of researchers and clinicians.

Activities: Walking and running

Accelerometer:

• A device that measures acceleration forces
• Placement of 3-axis Accelerometer: Anywhere around the ankle in any orientation as shown in Figure 1
• Sensitivity of the Accelerometer: (+-) 4g or more
• Sampling Frequency: Preferred - 128 Hz

Input data:

accX - accelerometer data from X - axis accY - accelerometer data from Y - axis accZ - accelerometer data from Z - axis

Input data format: The accelerometer signals should be in units of m/s^2 and need to be in .mat format [in Matlab file format].

IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.

The human walking is a periodic movementand can be divided into different number of phases. Here, we only tried to identify the beginning heel strick (HS) and end point or toe off (TO) of each gait cycle, and other temporal phases like swing, stance and stride can be inferenced from them.