Activity monitoring algorithm using a smartphone and/or wristband is offered as a service or as a licence agreement
A Slovenian research institute is offering an algorithm for activity recognition with a smartphone and an optional wristband. The algorithm uses context-specific machine-learning models and therefore does not depend on the position of the smartphone on the body. The algorithm outperforms several consumer devices in terms of accuracy in real-life setting. The algorithm is available via licencing or service agreement to companies and researchers developing wellbeing applications.
The research institute is looking for: a) industrial partners who are interested in obtaining a licence for activity monitoring algorithm – licencing agreement and b) companies or research institutions who would like to use the service (SaaS) through API - service agreement. These are especially: - companies that develop and sell healthcare and lifestyle applications; - companies that develop, produce and sell wearable wireless wellbeing, sport and fitness devices; - companies that offer solutions for remote patient or elderly monitoring, on-site professional healthcare monitoring and home/office/work environment monitoring; research institutions active in ambient intelligence, wellbeing, e-health, elderly support, sport tracking and other activity related research and development activities. Partners interested in service agreement will be able to use the activity algorithm in their application via SaaS service, which is offered by the Slovenian research institute. Partners interested in obtaining a licence for the activity algorithm are expected to be able to integrate the source code of the activity algorithm in their applications, e.g. on a smartphone, wrist-worn or any other smart wearable device. The authors of the activity algorithm are able to adapt the source code in case of specific requirements of a partner.
Accurate activity monitoring is required in domains where further reasoning or person-specific recommendations rely on the user's physical activity. These range from lighter topics such as sports and lifestyle to sensitive topics such as health. Current market offers smartphone applications and dedicated devices, whose scope is mostly limited to step counters (pedometers) with inaccurate estimation of energy expenditure or to monitoring only certain types of activities that the user must explicitly select. There are many activity-monitoring applications and devices on the market which mostly rely on simple techniques such as step counters, from which they estimate the energy expenditure, or which require the user to explicitly state which activity is being performed (i.e., they do not perform any automated activity recognition). The evaluation of consumer devices has shown that they also lack accuracy in real-life everyday activities. More accurate algorithms have been developed by researchers, but they remain in the academic domain and are not easily available for practical applications. The artificial intelligence researchers from a Slovenian public research institute have developed, evaluated and validated an algorithm for real-time continuous activity monitoring that utilises sensor data from a smartphone, a wrist-worn or a chest-worn device, and can fuse the data and decisions of the smartphone and one of the wearable wearable devices if both are present on the body. The activity monitoring consists of activity recognition and estimation of energy expenditure. It is done using machine-learning techniques. The design of the algorithm enables the user to put the smartphone in any pocket or in a bag and in any orientation, since the algorithm first detects the presence of the devices and then normalises the orientation. The normalised signal is used to recognise the location in case of the smartphone. The recognised context of the sensors is used to select the appropriate context-specific machine-learning model for activity recognition and estimation of energy expenditure. The technology is available either: a) under service agreement as a Software-as-a-Service (SaaS) through application programming interface (API) or b) under licencing agreement for the algorithm which can be run on a smartphone. Authors of the algorithm are computer-science experts employed at the Slovenian institution for research in sciences and technology. They are specialised in development of proprietary methods and algorithms for analysing wearable sensor data used mainly in the health domain, but applicable to other domains. The team has been among the finalists of a global competition for medical diagnostic devices. They have also won two international competitions on activity recognition. They are active in several projects on wearable monitoring of seniors for health, wellbeing and independent living, as well as supporting heart-failure patients using wearables and mobile applications. The researchers are looking for partners which are either: a) companies or research institutions which develop applications and would need the service (SaaS) through API (service agreement) or b) companies which are interested in obtaining a licence for implementation of the activity monitoring algorithm in their application (licencing agreement). In particular following companies or research institutions active in wellness and health domains are sought: - companies that develop and produce wearable wireless wellbeing, sport and fitness devices; - companies that offer solutions for remote patient or elderly monitoring, on-site professional healthcare monitoring and home/office/work environment monitoring.
Advantages and innovations
Advantages of the real-time continuous activity monitoring algorithm: - It self-detects the presence of the devices - It can monitor the activities with a smartphone, wrist-worn or chest-worn device alone, or with a combination of two devices (smartphone + wrist-worn device, smartphone + chest-worn device) - The devices can be worn in any orientation since the algorithm detects it and normalises it - The smartphone can be worn on three locations (trousers pocket, jacket pocket, bag) which are recognised automatically utilising machine-learning techniques - The activity-recognition models are trained on the most typical every day activities (rest, home chores, gardening, eating, etc.) as well as sports activities (walking, Nordic walking, running, cycling, etc.). - The models for estimation of energy expenditure are trained on the data labelled by the indirect calorimeter. - The evaluation results show that the algorithm outperforms several consumer devices.
Intellectual Property Rights (IPR)
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