Automatic fall detection of patients with chronic diseases using the TeNDER Health Tracking (HeTra) system

Photograph by Gustavo Hernández-Peñaloza

With an increasingly ageing population in Europe, the prevalence of cognitive impairments and heart disease represents a major social and health care issue. 1.2 million people in Europe are affected by Parkinson’s disease (PD), Alzheimer’s disease and other forms of dementia remain one of the biggest global public health challenges of our generation, and the World Health Organization estimates that cardiovascular diseases account for 31% of all global deaths.

TeNDER, currently running under the EU-funded Horizon 2020 programme, has developed and will shortly begin piloting an integrated care system. This system consists of assistive technologies that monitor vital signs, capture movement and gait, and recognise emotional states and changes. TeNDER’s driving motivation is to become a contributing factor in improving patients’ quality of life.

Here, we will focus on the specific ICT-based interventions designed to detect falls. The TeNDER system uses devices equipped with sensors to monitor and support the physical status of older adults.

Fall detection is one of the major challenges in the public healthcare domain, especially for the elderly. For this reason, reliable observation is necessary to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the healthcare industries. An effective detection system provides immediate support and helps reduce the medical care costs associated with falls [1, 2].

TeNDER will be capable of linking large amounts of relevant sensed information during the interventions, which can then be mined, analysed, and modelled. These analyses will give healthcare professionals additional information and pertinent feedback on-demand, to help them make more accurate and informed decisions about their patients’ care.

The continuous tracking and the personalised interventions will make the patient feel safer and more autonomous. Within the TeNDER project, all patient information from the various sensors will be collected, integrated, synchronised, and safely stored, while adhering to rigorous data protection procedures.

To achieve this, the Health Tracking (HeTra) tool (previously developed in the ICT4LIFE project [3]) has been extended in TeNDER to control the underlying Internet of Things hardware infrastructure and take over available sensor discovery (see Figure 1), device registration and management, and data collection. It will deliver robust and reliable data processing and an adaptation layer guaranteeing real-time communication and interoperability between the TeNDER cloud and the variety of sensing devices.

The HeTra tool now supports the novel solutions offered by affective computing detection, a feature that will be integrated into the analysis.

Figure 1 - Screenshot of the HeTra sensor discovery function. It shows the available sensors and their options.

HeTra monitors specific health characteristics, from direct situational information (e.g., activity and health markers) to periodical test results and medical observations. Additionally, HeTra incorporates a feedback mechanism extracts valuable conclusions about the health of a patient. During the pilots, the subsystem will help users prioritise which health characteristics to track.

HeTra uses the Body Tracking SDK from Kinect Azure to track multiple human bodies (see image on the left). This approach not only delivers raw data, it also analyses and extracts features that will be useful for subsequent evaluations.

Regarding fall detection, TeNDER can detect such events based on data acquired by several devices. More specifically, TeNDER acquires the coordinates of the body joints of a patient’s skeleton using (1) depth cameras (such as Kinect Azure), (2) the accelerometer data on the patient’s wrist (via Smart Watches), and/or (3) ambient sounds captured by microphones.

TeNDER then analyses the acquired data and can detect with high accuracy a fall event using deep learning methods [2] trained in large sets of data. Finally, it should also be noted that TeNDER is a modular system. Hence, a subset of the aforementioned devices suffice to detect falls. However, the use of data from more devices tends to increase the accuracy of the method.

By detecting, in real-time, the HeTra subsystem allows researchers to employ data from several types of sensors to alert the caregivers that a patient has fallen. Prompt alerts can help mitigate the effects of falls – which can often lead to a reduced quality of life – and patients and caregivers can feel safer and more independent in different settings.



[1] Mubashir, Muhammad, Ling Shao, and Luke Seed. “A survey on fall detection: Principles and approaches.” Neurocomputing 100 (2013): 144-152.

[2] T. Theodoridis, V. Solachidis, N. Vretos, P. Daras, “Human fall detection from acceleration measurements using a Recurrent Neural Network”, International Conference on Biomedical and Health Informatics Thessaloniki, Greece, 18-21 November 2017

[3] ICT4LIFE UPM DSS Repository. Available at:

TeNDER and the Quality of Life of people with chronic diseases

Quality of Life (QoL) is an important concept widely used in medicine, sociology, and psychology. The World Health Organization (WHO) defines QoL as an “individual’s perception of his or her position in life in the context of the culture and value system where they live, and in relation to their goals, expectations, standards, and concerns’’[1]. When applied in the context of healthcare, the concept of QoL refers mainly to health-related QoL (HR QoL), the component associated with health status, health care, and health-related social support.

HR QoL is a multidimensional concept, it covers physical aspects (for instance, physical symptoms and functions), mental factors (for instance, mental symptoms, psychological well-being, emotional status, and cognitive functioning), social components (like social well-being), as well as other elements (for instance, global judgments of health, satisfaction with care, health, and treatment and outcomes).

Some of these factors may influence the HR QoL of older people with chronic diseases. In elderly patients, QoL is mainly influenced by physical independence, physical and mental health, and by physical and behavioral symptoms; moreover, the level of autonomy in performing daily activities has a fundamental effect on the HR QoL of these patients.

Many patients with chronic diseases depend on care from their caregivers and relatives: the progression of the disease may increase the needs of the patients, which in turn may affect the HRQoL of caregivers and relatives.

TeNDER will create an integrated care ecosystem using micro-tools to assist people with Alzheimer’s, Parkinson’s, and cardiovascular diseases, and – where present – comorbidities. These micro-tools will be able to recognise the status of a person and thus adapt the system to the person’s needs via a multi-sensorial system (even in the most severe cases), and match with clinical and clerical patient information, while preserving privacy, monitoring the ethical principles, providing data protection and security.  By combining user-friendly technologies and substantial research experience, our project aims to help improve the HRQoL of patients and those who surround them.



[1] The WHOQOL Group. (1995). The World Health Organization Quality of Life Assessment (WHOQOL): Position paper from the World Health Organization. Social Science and Medicine, 41(10), 1403–1409.