The LPMS-ME1’s Maker Edition is miniature-sized with just 12 x 12 x 2.6 mm.
We proudly present you our latest development! The LPMS-ME1 is our smallest motion sensor so far, with just 12 x 12 x 2.6 mm it is tiny! Despite its size this powerful 9-axis inertial measurement unit (IMU) has several sensors integrated, for example a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer. And this miniature motion sensor certainly comes at low cost.
It is very easy to assemble and can be conveniently embedded in the system of your choice. Due to its size it is perfect for your design ideas and development projects. Just to to give you some inspiration, it can be used for human motion capture or sports performance evaluation, for various sorts of Internet of Things (IoT) devices, and can be used to control unmanned aerial vehicles. You can even fly a drone with it!
Have a look at more specifications in our data sheet. This sensor comes with our own LpmsControl utility software and a one-year warranty service.
Get the LPMS-ME1 Maker Edition for your own innovations! Find a distributor of your choice or order online at Zenshin Tech.
Deterministic Analysis vs. Machine Learning for Context Analysis
Machine learning for context analysis and artificial intelligence (AI) are important methods that allow computers to classify information about their environment. Today’s smart devices integrate an array of sensors that constantly measure and save data. On the first thought one would image that the more data is available, the easier it is to draw conlusions from this information. But, in fact larger amounts of data become harder to analyze using deterministic methods (e.g. thresholding). Whereas such methods by themselves can work efficiently, it is difficult to decide which analysis parameters to apply to which parts of the data.
Using machine learning techniques on the other hand this procedure of finding the right parameters can be greatly simplified. By teaching an algorithm which information corresponds to a certain outcome using training and verification data, analysis parameters can be determined automatically or at least semi-automatically. There exists a wide range of machine learning algorithms including the currently very popular convolutional neural networks.
Figure 1 – Overview of the complete analysis system with its various data sources
Context Analysis
Many health care applications rely on the correct classification of a user’s daily activities, as these reflect strongly his lifestyle and possibly involved health risks. One way of detecting human activity is monitoring their body motion using motion sensors such as our LPMS inertial measurement unit series. In the application described here we monitor a person’s mode of transportation, specifically
- Rest
- Walking
- Running
- In car
- On train
To illustrate the results for deterministic analysis vs. machine learning for context analysis approach we first implemented a state machine based on deterministic analysis parameters. An overview of the components of this system are shown in Figure 1.
Figure 2 – Deterministic approach
The result (Figure 2) is a relatively complicated state machine that needs to be very carefully tuned. This might have been because of our lack of patience, but in spite of our best efforts we were not able to reach detection accuracies of more than around 60%. Before spending a lot more time on manual tuning of this algorithm we switched to a machine learning approach.
Figure 3 – Machine learning approach
The eventual system structure shown in Figure 3 looks noticeably simpler than the deterministic state machine. Besides standard feature extraction, a central part of the algorithm is the data logging and training module. We sampled over 1 milion of training samples to generate the parameters for our detection network. As a a result, even though we used a relatively simple machine learning algorithm, we were able to reach a detection accuracy of more than 90%. A comparison between ground truth data and classification results from raw data is displayed in Figure 4.
Figure 4 – Result graphs comparing ground truth and analysis output for ~1M data points
Conclusion
We strongly belief in the use of machine learning / AI techniques for sensor data classification. In combination with LP-RESEARCH sensor fusion algorithms, these methods add a further layer of insight for our data anlysis customers.
If this topic sounds familiar to you and you are looking for a solution to a related problem, contact us for further discussion.
New worldwide distribution by Zenshin Technology Ltd
Getting our IMU sensors online has just become so much easier. Create your own innovations and simply order at our new worldwide distributor: Zenshin Technology Ltd is an online shop operating from Hong Kong. Have a look at our next generation IMUs and explore the optimized features of for example, the LPMS-CURS2 (9-axis motion sensor with USB, CAN bus and UART connectivity) or the LPMSs-CANAL2 (9-axis IMU with CAN connectivity and waterproof housing).
In addition to our distributors in every region, you can get LP-RESEARCH’s IMU sensors now from Zenshin Tech. Order comfortably with worldwide express shipping from here: https://zenshin-tech.com
