The state-of-the-art competing technologies in the domain of presence sensing and touchless gestures include vision based systems using cameras, time-of-flight (ToF) sensors using infrared (IR), radio frequency systems such as radar and WiFi, and touch screens. Without exception, these technologies require dedicated hardware sensors, and additional processing software, which means there is substantial cost and challenges in integration. Certain technologies such as camera, ToF sensors and touchscreens have various limitation in range and coverage. Cameras in addition lead to user privacy concerns.
There has been significant progress made compared with the competing technologies through the project. The key points are summarised as follows:
a. developed fully embedded, integrated ultrasound-based virtual sensors for presence sensing and touchless gestures which do not require additional hardware and instead use only built-in speaker and microphone
b. validated robust performance achieved using machine learning
c. building cloud-based ML platform which enables easy scale-up
d. supporting sensor fusion which incorporates other sensors to further improve performance and provide additional functionalities.
At the end of the project, the cloud-based ML platform will be ready to launch. Through specifically designed interface, customers will be able to provide use case definitions, product and performance specifications, all can be done remotely. Elliptic Labs then provides a software integration package specific to their devices, in addition to a set of machine learning classifiers, which are built using the collected training data sets and various signal processing modules. With the set of web tools provided, customers also have the possibility to gauge and optimise performance by collecting data themselves. Through out the process, Elliptic engineers will provide continuous customer support for specific integration and optimisation.
Because the platform is cloud-based, and is built on top of Elliptic's ever expanding knowledge and data base, it can benefit the customer by greatly reducing the deployment time and cost, while at the same time allows Elliptic to support its ever growing customer base with superior products. The expected economic impact includes entry in the global billion dollar IoT market, gross profit, generating employment and stimulation of the R&D activities in machine learning and ultrasound technology.
In addition, waste of electrical and electronic equipment (WEEE) is one of the fastest growing waste streams in the EU. It grows at 3-5 % per year and is expected to account for more than 12 million tonnes by 2020. In 2012 the EU approved the WEEE Directive to reduce the quantity of such waste to be disposed of. The IMIR-UP Phase 2 project will contribute to the WEEE Directive as a software-only solution that does not need supplemental hardware, which is not the case of the other alternative technologies. Furthermore, the EU has set different directives to improve energy efficiency in electronic products (e.g. the EU Directive 92/75/EC that established an energy consumption labelling scheme) and lighting systems (e.g. EU Directive 874/2012 that established energy labelling regulation for lamps and luminaires). INNER REFLECTION will contribute to improve energy efficiency of electronic products and lighting systems.
