2022/10/28-2023/11/10
What I did for this week:
1. Data Filtering: Low Pass and High Pass Filters
- Low Pass Filter: This type of filter allows signals with a frequency lower than a set cutoff point to pass through and attenuates frequencies higher than this point. It's useful in motion data to remove high-frequency noise or to smooth out the data.
- High Pass Filter: Conversely, this filter allows signals with a frequency higher than a certain cutoff point to pass through and attenuates lower frequencies. It's used to remove low-frequency drift or bias in the data.
Both filters are essential in processing motion data to ensure that the data reflects the true movement without interference from noise or unwanted frequencies.
2. Data Interpolation
- Purpose: It's used to estimate missing data points within a series of known data points.
- Use in Motion Data: In cases where the motion data is incomplete or sampled at irregular intervals, interpolation can help in creating a continuous and uniform set of data points. This is crucial for accurate analysis and modeling.
3. Normalization
- What It Is: Normalization involves scaling the data values so that they fit within a specific range, often 0 to 1 or -1 to 1.
- Importance: This step is crucial when comparing or combining data that are of different scales or from different sources. It ensures that one feature does not dominate others simply because of its scale.
4. Offset Correction
- Definition: This process involves adjusting the data to correct for any systemic biases or offsets.
- Application in Motion Data: This is particularly important if the sensor capturing the data has a known bias or if the initial conditions are not zeroed out. It helps in ensuring the accuracy of the data.
5. Time Synchronization
- Why It's Important: When collecting data from multiple systems or sensors, it's crucial that the data are synchronized in time.
- Reason: This is because any time lags or misalignments between different data sources can lead to incorrect interpretations or analysis. For example, in motion analysis, synchronization ensures that the movements recorded by different sensors at supposedly the same moment are actually aligned in time.