Not everyone can afford a personal trainer to keep them motivated and on track in the gym or on the sports field. For those who want personalised guidance without the expense, the sensor industry is stepping up to the plate. By tackling some of the conventional design challenges it is in line to be the next disruptor in wearable technology.
Smartwatches have been monitoring our heart rates, step count and calories burned since the first FitBit was launched in 2009. Smartwatches followed in 2014 and the healthcare market has never looked back.
The designs of fitness trackers and smartwatches has been refined over the years, to become the slim models we know today but they are still considered too bulky for many forms of active exercise.
Design challenges
Molex and Avnet commissioned Dimensional Research to survey design engineers for the Future of Diagnostic Wearables: The Future of Medical Monitoring report.
Respondents identified the size of devices, including the sensing elements and connectors as one of the major design challenges. There were also concerns around a simple user interface, power management, signal quality and thermal management.
A sensor may record changing environments as well as measure pressure and motion This requires them to be small to minimise the final form factor but also reliable and stable in operation in different conditions.
The evolution of sensor design has resulted in integrated sensors designed in to less cumbersome end products that can easily be worn by active users.
Take a look at running
French company, ActiveLook, a division of OLED microdisplay manufacturer, MICROOLED, has made great strides in implementing AR into smart glasses. Its heads-up display technology is embedded into smart glass frames and lenses to project data onto the lens.
The microdisplay module is near invisible and does not obstruct the wearer’s view of the horizon. It is based on a monochrome AMOLED display, with 304 x 256 pixels resolution and consumes less than 1mW, weighs 6g and has a battery life of more than 12 hours.
The OLED technology allows connected apps to display data on the lens for the glasses wearer to monitor parameters such as distance, time, speed, pace or their heart rate via a smart chip integrated into the frame of the eyewear.
The company has partnered with app providers, such as Openrunner, and iKinesis. Openrunner shows routes in real time to navigate as well as track running, hiking or cycling routes.
iKinesis’ eponymous running coach app is connected by a Kapsule, a nine-axis inertial sensor. Athletes attach the Kapsule to the front of the running shoe and the app then analyses data parameters, such as pronation, foot strikes, speed, propulsion efficiency and cadence.
The idea is for the runner to concentrate on the chosen route while receiving real-time data to correct gait. The sensor communicates via Bluetooth Low Energy 5.
Later, the app also uses data from the Kapsule to reproduce the runner’s movement in 3D, to show the centre of gravity, strike attack and the forces involved in specific areas such as the knees, hips and ankles. It also displays the iKinesis index i.e., distance, speed, duration as well as technique such as stride and running cadence. Algorithms interpret the data to analysis areas of most stress to avoid injury. An interpretation of the running technique can create a prevention routine from a library of exercises, classified as stimulation, stretching and massage.
The ActiveLook device connects to a smartphone, watch or specific sensors via Bluetooth Low Energy to conserve power consumption. It has, for example, also teamed up with Finnish sports watch manufacturer, Suunto. Its smartwatch’s sensors will gather and process data which will be displayed on the connected near-eye display. It can, for example, send “road” alerts to be displayed on the eyewear, for the runner to adapt their effort level or change equipment according to conditions.
In the swim of things
The obvious design issue for swimmers is to protect the wearable device while being submerged in chlorinated water and this tends to be addressed with encapsulation techniques.
Bosch-Sensortec for example has designed the BMP384 pressure sensor in a metal lid LGA with a gel-filled cavity to increase robustness against water, liquids and chemicals. It is compact at 2.0 x 2.0 x 1.0mm and can measure water levels and pressure.
It operates at a range of 300 to 1,250hPa, with a current consumption of 3.4µA and 2.0µA in sleep mode.
An accelerometer in a waterproof wristband can detect when swimming has begun and is usually connected to the cloud for analytics. Stroke by stroke feedback, however, requires the collected data to be transmitted and processed remotely, which can prove expensive in terms of the data bill. It also requires the device to be paired to a smartphone or connected to the internet which could be expensive or impractical. Storing and processing the data requires both memory and a processor, usually a GPU for speed, which can increase the cost of the end product.
Bosch-Sensortec developed a swimming tracker device using its BHI260AP smart sensor and sensor fusion. The smart sensor integrates a three-axis accelerometer and a three-axis gyroscope with a floating-point microcontroller to provide raw sensor data and to run AI functions. Using an application processor reduces processing demand, conserving system power consumption.
The tracker’s software uses the motion sensor date to determine that the swimming session is in progress, without the swimmer having to switch it on or activate it in any way. The software determines which swim stroke is being used and records the number of strokes, laps and any breaks between laps. Using lap and stroke counts with the swimming stroke category allows the wearable device to calculate performance, based on the number of seconds to swim a lap plus the number of strokes. This data allows swimmers to monitor performance and identify any areas of weakness. It is at this point that a human coach might be needed to perfect the style or technique.
Add some AI
AI can assess the height of the swimmer, the length of their limbs and the speed and power at which their arms and legs move through the water. It can also track any fluctuations in the speed at which the limbs move to track tiredness or weak areas.
The AI software can be configured according to the computational resources available, or the budget. For example, latency or power consumption can be traded to meet the desired code size.
Another design advantage of using AI software is that it can be configured to track a specified standardised swimming style or adapted according to the swimmer’s ability or performance class; professional or amateur, for example.
In time, AI could be used to recognise and adapt to the age, height and ability of the swimmer and provide analysis for improvement in technique and/or performance. For now, coaches can view data or 3D images generated by AI, to identify areas for improvement, such as where a running gait might apply too much weight to a joint for it to be corrected or to perfect a swimmer’s technique to improve speed, propulsion or turns.
IDTechEx has identified miniature pressure sensors as a further opportunity for wearable sports devices.
Worn on the wrist or in the ear, devices can use these sensors to measure push-ups, or strength training for gym goers to track how much they are lifting without relying on a smartphone.
