What question did this study set out to answer?

To review emerging advancements and challenges in combining motion sensors and machine learning algorithms to detect fall risk in prefrail individuals prior to their first fall.

July 11, 2022Journal of NeurologyOpen Access

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

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Authors

IBIoannis BargiotasUniversité Paris-Saclay DWDanping WangUniversité Paris Cité JMJuan MantillaUniversidad Pontificia Bolivariana

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Implication

Review highlights machine learning combined with wearable sensors for first-time fall prediction in prefrail individuals, indicating opportunities for early clinical intervention.

Key Points

To review emerging advancements and challenges in combining motion sensors and machine learning algorithms to detect fall risk in prefrail individuals prior to their first fall.
Synthesized developments across non-wearable modalities (force platforms, 3D motion-tracking systems) and wearable devices (accelerometers, gyroscopes, inertial measurement units).
Evaluated computational protocols integrating these sensor platforms with machine learning analytics to assess subtle biomechanical indicators of early frailty.
Combining non-wearable and wearable sensor systems with machine learning algorithms enables the detection of subclinical gait and balance impairments during the prefrail stage.
Preemptive prediction of fall risk before an initial incident occurs presents a viable pathway to implement targeted preventive care and preserve patient autonomy.

Cite This Study

Bargiotas et al. (2022) studied this question.

synapsesocial.com/papers/6a9ec98a37e106657b192ded https://doi.org/10.1007/s00415-022-11251-3

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Implication

Review highlights machine learning combined with wearable sensors for first-time fall prediction in prefrail individuals, indicating opportunities for early clinical intervention.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Discussion

Member takes

Implication

Key Points

Cite This Study

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Discussion

Member takes

Implication

Key Points

Cite This Study

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