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SafeWalk

Senior-oriented Robotic Devices for

Personalized Fall Prediction and Prevention

Our Motivation

The high number of fall-related injuries are one of the most common health concerns especially in elderly people, being a marker of frailty, acute and chronic health impairment. According to the World Health Organization (2018), falls are the second main reason of death by accident worldwide, killing an estimated 646 000 individuals per year. Moreover, 37.3 million falls are severe enough to require medical attention each year. As natural effects after a fall comes the fear of falling again, depression, social isolation, physical decline, or feelings of helplessness. Slow interventions after a fall may exacerbate the whole situation and the mentioned effects. Also, the treatment of fall-related injuries has economic repercussions. Only in the United States of America, in 2000, $19 billion were spent on the direct medical costs of fall-related injuries. In 2015, this value has risen to more than $31 billion in the Medicare alone, and in 2020, it surpassed the $40 billion barrier.

 

Proposed Solution

SafeWalk’s main goal is to contribute with the development of anti-fall strategies and a fall risk assessment tool that can work in collaboration with daily life accessories mainly used among the elderly and patients with gait disorders. It is intended to monitor subjects continuously, detect abnormal situations with a high risk of falling, and act to prevent falls by avoiding them or at least minimize related injuries. At the moment, focus has been on canes and walkers due to their popularity and usability within the mentioned population. Our project is divided in three phases:

  1. Data Collection: With several devices properly distributed among target population according to their mobility level, we pretend to collect data in different instituitions and provoke falls in our laboratory.
  2. Development of algorithms: We intend to develop new AI-based algorithms with data collected previously.
  3. Development of devices: Improve and equip the devices with the algorithms developed.

 

 

Application Scenarios

SafeWalk devices are specially designed to work in healthcare institutions, namely hospitals, rehabilitation centres, long-term care units, and nursing homes. These applications were thought and designed to assess the gait and balance disorders of patients with low levels of mobility. Furthermore, we hope that these device can also assist the gait of elderly people towards healthy ageing.

Our Team

Currently, SafeWalk team involves a PhD student and six MSc students in several areas of Engineering:

  • Nuno Ferrete Ribeiro – MSc in Biomedical Engineering. PhD Student in Leaders for Technical Industries/Engineering Design and Advanced Manufacturing (LTI/EDAM) at MIT Portugal
  • Rafael Ferreira – MSc Student in Biomedical Engineering
  • Luís Martins – MSc Student in Biomedical Engineering
  • Henrique Pires – MSc Student in Electronic Engineering
  • Rodolfo Cerqueira – MSc Student in Physics Engineering
  • Raimundo Barros – MSc Student in Informatics Engineering
  • João Nunes – MSc Student in Biomedical Engineering

Alumni

SafeWalk team does not forget its past members that contributed to the development of this project:

  • Pedro Mouta – MSc in Biomedical Engineering
  • Ana Pereira – MSc in Biomedical Engineering
  • Rúben Durães – MSc in Biomedical Engineering
  • Ricardo Andrade – MSc Student in Biomedical Engineering
  • Pedro Ferreira – MSc Student in Biomedical Engineering

Our Partners

The SafeWalk project involves a company, Orthos XXI, Unipessoal, Lda. We also have the support of several nursing homes, which allows us to test our solutions with real patients who need a walking aid andor a fall risk assessment tool, namely:

  • Fundo Social – Município de Braga
  • Centro Social e Paroquial de Sobreposta
  • Cruz Vermelha Portuguesa – Braga
  • Centro Social e Paroquial do Vale
  • Associação Cultural e Recreativa de Cabreiros
  • Centro Social e Paroquial de Ferreiros
  • Centro Social Padre David de Oliveira Martins
  • Resisénior

 

Our Publications

Journal Articles
  • Rafael Neto Ferreira, Nuno Ferrete Ribeiro and Cristina P. Santos, ”Fall Risk Assessment Using Wearable Sensors: A Narrative Review,” in Sensors (Switzerland), vol. 22, no. 3, pp. 984, Jan.2022. [IF(2020) – 3.576; Q2 – Analytical Chemistry, Q2 – Atomic and Molecular Physics, and Op-tics, Q3 – Biochemistry, Q2 – Electrical and Electronic Engineering, Q2 – Information Systems, Q2 -Instrumentation, Q2 – Medicine (miscellaneous)].
  • Nuno Ferrete Ribeiro and Cristina P. Santos, ”Two Fall-Related and Kinematic Data-Based Approaches for an Instrumented Conventional Cane,”in IEEE Transactions on Human-Machine Systems, vol. 51, no. 5, pp. 554-563, Oct. 2021. [IF(2020) = 2.968; IF(2019) = 3.374; Q1 -Artificial Intelligence, Q1 – Computer Networks and Communications, Q1 – Computer Science Applications,Q1 – Control and Systems Engineering, Q1 – Human-Computer Interaction, Q1 – Human Factors andErgonomics, Q1 – Signal Processing].
  • Nuno Ferrete Ribeiro, Pedro Mouta and Cristina P. Santos, ”Two Kinematic Data-Based Approaches for Cane Event Detection,”in Journal of Ambient Intelligence and Humanized Computing, May 2021. [IF(2020) = 7.104; IF(2019) = 4.594; Q1 – Computer Science (miscellaneous)].
  • Nuno Ferrete Ribeiro, João André, Lino Costa, and Cristina P. Santos, “Development of a Strategy to Predict and Detect Falls Using Wearable Sensors,” Journal of Medical Systems, vol. 43, no. 5,p. 134, Apr. 2019. [IF(2018) = 3.223; IF(2019) = 4.136; IF(2020) = 5.227; Q2 – Health Informatics,Q2 – Health Information Management, Q2 – Information Systems, Q2 – Medicine (miscellaneous)]
Conference Papers
Book Chapters