Mobile Robotic Balance Assistant (MRBA): a gait assistive and fall intervention robot for daily living.
Lei Li, Ming Jeat Foo, Jiaye Chen and 8 others
PMID 36859286WHAT IT FOUND
A prototype robot slowed walking speed and step length in users but detected 94% of simulated falls in healthy adults with no false alarms.
It has not been tested on patients with balance impairments, so its real-world safety and effectiveness remain unknown.
Key findings
01The robot significantly reduced walking speed and step length in healthy subjects and the single patient with spinal cord injury.
02The fall detection algorithm identified approximately 94% of self-induced falls in healthy young adults with a 0% false alarm rate.
03When applied to the patient with spinal cord injury, the fall detection algorithm produced false alarms for all walking trials.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
The study primarily involved healthy young adults, who have better balance than the target clinical population. Only one patient with spinal cord injury was tested, and the device's algorithm failed completely for this individual. The falls were simulated (either induced by a machine or self-initiated), which may not replicate the unpredictability of real-world falls. The robot significantly slows down walking speed, which may reduce the training dose or efficiency for rehabilitation purposes. The current force-based detection algorithm is prone to false alarms in patients who rely more on body weight support.
Declared interests
Funded by the SG Health Assistive and Robotics Programme.
The easy way to misread this
Do not assume this robot is safe or effective for patients with balance impairments. The algorithm produced false alarms for every trial in the single patient tested, and the device significantly slowed walking speed in all users.