Glance is a wearable brand initiated by Anvil Ng, who was the founder of CWB Tech Limited. It is a hand band designed for runners who want to know more about his running performance.
Internally, it is an SDK for R&D team to capture motion sensor data. The SDK was also adopted by HKUST engineering school as a course equipment.
I have acquired two unique knowledge from this project. One is ultra low power design, the other is motion modelling.
In Glance SDK, I have to keep my pedometer algorithm active with power budget under 80uA@3.7V. In wearable world, power budget is the key success factor. If Apple watch play time can last for a week, I believe there will be much more consumers using it.
Motion analysis and modelling is an interesting topic. Here are some demonstrations of the experiment I conducted with Glance SDK. If you are interested, free feel to Skype me.
Knee shock experiment (click the image to view the video)
TaiChi workout (click the image to view the video)
(Special thanks to Master Titan Lam)
Snooker Waggle Analyzer
(Special thanks to James O’Shea)
Swimming motion capture is tricky because water absorbs most 2.4GHz wireless signals (e.g. Wi-Fi, Bluetooth, BLE and etc). You will need a non-volatile memory storage (e.g. SPI NOR) in your wearable.
This is my breaststroke motion capture. There are some swimming wearable in the market, which applies accelerator only. However, swimming is a combination of angular force and linear force. With the additional gyroscope sensor, much more information can be captured and analyzed.
Sleeping motion capture and analysis is relatively simple. All your concern is to keep the power consumption as low as possible and having an efficient data structure to store aggregated impulse information.
Pedometer algorithm is easy when the wearable is on your body trunk. There are a lot of research papers talking about it. However, when the wearable is on your wrist, there is additional hand movement force for the algorithm to handle.
Since the accelerator chip vendors (e.g. STMicroelectronics) have bundled pedometer algorithm in their solution, application software engineer needs not to touch the detail. However, it is still good to know how your walk/run motion look likes. In reality, the motion cycle is not just a peak and a valley. There are many walking gesture introducing multiple peaks. Traditional pedometer algorithm are especially weak in running (hand motion vector dominates) and walking while playing Pokemon Go (suppress motion amplitude to hold the mobile phone steady). There are still some room for pedometer market leaders to tune the algorithm.
Putt with Glance is possible with similar accuracy. The major problem is that you cannot wear Glance at your wrist. It is because the golfer may twist his wrist during the swing and it is relatively difficult to model with my algorithm. In other words, mounting the wearable gracefully on the club is the remaining product design issue.