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Using musical cues to assess risk of fall of older adults in outdoor environments
Currently, individuals at risk of falling are identified through clinic- and lab-based assessment of gait and movement function. These tests evaluate changes in motor skills in a steady environment free of disturbances, while most falls occur during real life environments with disturbances such as obstacles and uneven walking surfaces, thus they are not precise enough for the quantification of fall risk. A sensitive marker for fall risk can therefore be identified through assessing walking behavior in real-life.
Keywords: fall risk, biomechanics, sensors, wearables, music
According to the World Health Organisation, globally, 28-35% older adults (>65 years) fall each year and is the leading cause of injury and sometimes leading to fatal injuries in older adults. According to the Swiss Council for Accident Prevention, falls are the most common type of accident sustained by older people (approximately 41 per 1000 men and 77 per 1000 women)and fall-related injuries generate health-care costs of about CHF 1.4 billion per year in Switzerland alone. Predicting and preventing falls are thus of supreme importance in the care of older adults.
Towards this goal, we have designed a study to investigate whether walking behavior in real-life provide biomarkers to predict future falls in older adults. Equipped with IMU sensors (www.zurichmove.com), our participants will perform a series of movement tasks (quite standing, sit-to-stand, twice six minutes of walking) in a public place (town square, parks etc.). In one of the walking trial, participants will listen to musical cues with unanticipated increased and/or decreased tempo of music via a hearable technology and try to synchronize their steps to those cues. We will focus on capturing participant’s adaptive movement behavior to common disturbances in outdoor setting(e.g., walking surface irregularities, encounters with people, objects, and moving around obstacles) and their ability to match walking pattern to the music to assess the risk of falls in the individuals.
Your Background: Student of Health Science and Technology, Biomedical Engineering, Movement Science or Physiotherapy with a strong interest in technology and its applications in health science and technology
According to the World Health Organisation, globally, 28-35% older adults (>65 years) fall each year and is the leading cause of injury and sometimes leading to fatal injuries in older adults. According to the Swiss Council for Accident Prevention, falls are the most common type of accident sustained by older people (approximately 41 per 1000 men and 77 per 1000 women)and fall-related injuries generate health-care costs of about CHF 1.4 billion per year in Switzerland alone. Predicting and preventing falls are thus of supreme importance in the care of older adults.
Towards this goal, we have designed a study to investigate whether walking behavior in real-life provide biomarkers to predict future falls in older adults. Equipped with IMU sensors (www.zurichmove.com), our participants will perform a series of movement tasks (quite standing, sit-to-stand, twice six minutes of walking) in a public place (town square, parks etc.). In one of the walking trial, participants will listen to musical cues with unanticipated increased and/or decreased tempo of music via a hearable technology and try to synchronize their steps to those cues. We will focus on capturing participant’s adaptive movement behavior to common disturbances in outdoor setting(e.g., walking surface irregularities, encounters with people, objects, and moving around obstacles) and their ability to match walking pattern to the music to assess the risk of falls in the individuals.
Your Background: Student of Health Science and Technology, Biomedical Engineering, Movement Science or Physiotherapy with a strong interest in technology and its applications in health science and technology
Validation of new approaches for assessing movement behavior in real life and subsequently using them to identifying individuals at risk of falling.
Your Tasks - Testing of the experimental setup in a small group of young adults and refinement (with the help of our development team); Experimentally validate the developed assessment in a group of older adults
Available Resources - Experimental setup: sensor-based movement analysis system - musical cueing setup - field laptop; Basic code base and analysis pipelines; Ethical approval for the study
Your Workplace - Your main place of work will be Laboratory for Movement Biomechanics, ETH Zurich (GLC Campus) but measurements require working in the field (various places in Zurich city).
Validation of new approaches for assessing movement behavior in real life and subsequently using them to identifying individuals at risk of falling.
Your Tasks - Testing of the experimental setup in a small group of young adults and refinement (with the help of our development team); Experimentally validate the developed assessment in a group of older adults
Available Resources - Experimental setup: sensor-based movement analysis system - musical cueing setup - field laptop; Basic code base and analysis pipelines; Ethical approval for the study
Your Workplace - Your main place of work will be Laboratory for Movement Biomechanics, ETH Zurich (GLC Campus) but measurements require working in the field (various places in Zurich city).
Dr Deepak Ravi (deepak.ravi@hest.ethz.ch)
Ms. Angela Frautschi (angela.frautschi@hest.ethz.ch)
Dr Navrag Singh (navragsingh@ethz.ch)
Dr Deepak Ravi (deepak.ravi@hest.ethz.ch) Ms. Angela Frautschi (angela.frautschi@hest.ethz.ch) Dr Navrag Singh (navragsingh@ethz.ch)