The pedometer is a useful tool for tracking daily physical activity, measuring the number of steps taken, estimating walking distance, and calculating calories burned. It also allows users to monitor their fitness intensity, exercise level, and metabolic rate in real time. Early mechanical pedometers relied on the vibrations caused by movement to trigger a mechanical switch, which detected each step. While these devices were inexpensive, they suffered from poor accuracy, low sensitivity, and large size, making them unsuitable for integration into modern systems.
With the advancement of MEMS (Micro-Electro-Mechanical Systems) technology, inertial sensors based on MEMS have become widely used. These sensors are compact, energy-efficient, and highly accurate. An electronic pedometer using a MEMS accelerometer can measure human motion and calculate steps through software algorithms. This approach overcomes the limitations of mechanical pedometers, enabling precise detection of gait and real-time data output for motion analysis.
**1. Human Motion Model**
Analyzing the acceleration signals from gait patterns is an effective way to extract key parameters of human walking. Walking involves three main components: forward, lateral, and vertical motion, as shown in Figure 1. The LIS3DH is a three-axis (X, Y, Z) digital output accelerometer that aligns with these directions.
In each step, the vertical acceleration increases when the foot lifts off the ground, peaks at the highest point, and then decreases as the foot lands. The forward acceleration is influenced by friction between the foot and the ground, increasing when the foot makes contact and decreasing when it lifts. These patterns help identify steps accurately.
However, the placement of the device may vary, so the sensor axes might not always align with the body's motion. To address this, the peak-to-peak value of the acceleration is used to determine the most effective axis. Still, this method can miss some steps. A more reliable approach involves summing the three-axis signals for better step counting.
**2. Algorithm Design**
Figure 3 shows the acceleration data from the LIS3DH during a walking experiment. The Z-axis (vertical direction) exhibits clear periodicity, with minimum values corresponding to the moment the foot leaves the ground and maximum values when the foot reaches its highest point.
To improve accuracy, the original signal is filtered using FFT (Fast Fourier Transform) to remove noise. Since human walking cadence typically ranges from 0.5 to 5 steps per second, the algorithm focuses on this frequency range. By analyzing experimental data, a dynamic frequency range (f1, f2) was established to enhance performance.
After filtering, the acceleration curve becomes smoother, with clearer peaks and troughs, making it easier to count steps. Even when the device is stationary, the peak values are much smaller than those during walking, allowing the system to distinguish between actual steps and noise.
Based on the analysis, the algorithm identifies key characteristics of a step cycle, such as one maximum and minimum value, a rising and falling interval, and alternating peaks and troughs above a certain threshold. These criteria ensure accurate step detection, even during rapid or slow movements.
**3. Hardware Implementation**
Figure 7 illustrates the hardware block diagram of the system. The LIS3DH accelerometer outputs digital signals, eliminating the need for analog-to-digital conversion. It communicates with the microcontroller via SPI or I2C interfaces. The control module includes an LCD display, a microcontroller, a keyboard, and a power supply, all working together to process acceleration data and display the step count.
To evaluate the pedometer’s accuracy, tests were conducted under different walking speeds—slow, normal, and fast—with the Z-axis facing upward. Each test involved 100 steps, and the results are shown in Table 1.
**4. Conclusion**
The LIS3DH accelerometer features a compact 3mm × 3mm × 1mm package, making it ideal for integration into mobile devices like smartphones, remote controls, and gaming consoles. Its three-axis digital output allows users to wear the pedometer anywhere on the body. The system offers high precision and stability, adapting well to varying walking conditions. Overall, it provides a reliable solution for real-time step counting and motion analysis.
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