iStoppFalls – Innovative Fall Prevention

#Digital Healthcare (MA) #Student projects

This project aims to support older people by using a combination of bed-exit sensors and smart speakers to prevent them from falling.

iStoppFalls – Innovative Fall Prevention

Falls are a significant safety issue in healthcare settings, particularly for the elderly population and those with cognitive impairments, who are at an increased risk due to the predicted increase in life expectancy (1).

Statistics show that 35% of individuals aged 65 and older experience at least one fall per year, and there are approximately 600,000 falls resulting in death each year worldwide (2). The fall rate in hospitals ranges from 3.5 to 18 falls per 1000 occupied bed days, with 3 to 37% of patients experiencing a fall during hospitalisation (3).

The use of speech assistants for fall prevention in older adults is a research area that shows a gap, as there is limited literature on this topic (4). The implementation of a bed-exit sensor in combination with a smart speaker is intended to address this issue and support the elderly population and those with cognitive impairment in preventing falls. Balaguera et al. (2017) reduced the incidence of bed falls among patients in healthcare settings by using a bed-exit sensor combined with an audible message.

Project goal

In conclusion, the proposed project aims to address the significant issue of falls in healthcare settings, specifically for older adults and individuals with cognitive impairment, through the use of a bed-exit sensor and a smart speaker. The goal is to investigate the potential use of speech assistants as a tool for fall prevention and determine if they can be used as assistance to give caregivers more time. Meanwhile, the language and statements used by the speech assistant should be adapted to the cognitive abilities of the patient. The project will also determine which bed-exit sensors are advisable, with a focus on low-cost options due to resource limitations.


  1. Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors (Basel). 2021;21(15).
  2. Schoberer D, Breimaier HE, Zuschnegg J, Findling T, Schaffer S, Archan T. Fall prevention in hospitals and nursing homes: Clinical practice guideline. Worldviews Evid Based Nurs. 2022;19(2):86-93.
  3. Cooper K, Pavlova A, Greig L, Swinton P, Kirkpatrick P, Mitchelhill F, et al. Health technologies for the prevention and detection of falls in adult hospital inpatients: a scoping review. JBI Evid Synth. 2021;19(10):2478-658.
  4. Kosse NM, Brands K, Bauer JM, Hortobagyi T, Lamoth CJ. Sensor technologies aiming at fall prevention in institutionalized old adults: a synthesis of current knowledge. Int J Med Inform. 2013;82(9):743-52.