Mobile Robotic Balance Assistant (MRBA): a gait assistive and fall intervention robot for daily living.

BACKGROUND: Aging degrades the balance and locomotion ability due to frailty and pathological conditions. This demands balance rehabilitation and assistive technologies that help the affected population to regain mobility, independence, and improve their quality of life. While many overground gait rehabilitation and assistive robots exist in the market, none are designed to be used at home or in community settings. METHODS: A device named Mobile Robotic Balance Assistant (MRBA) is developed to address this problem. MRBA is a hybrid of a gait assistive robot and a powered wheelchair. When the user is walking around performing activities of daily living, the robot follows the person and provides support at the pelvic area in case of loss of balance. It can also be transformed into a wheelchair if the user wants to sit down or commute. To achieve instability detection, sensory data from the robot are compared with a predefined threshold; a fall is identified if the value exceeds the threshold. The experiments involve both healthy young subjects and an individual with spinal cord injury (SCI). Spatial Parametric Mapping is used to assess the effect of the robot on lower limb joint kinematics during walking. The instability detection algorithm is evaluated by calculating the sensitivity and specificity in identifying normal walking and simulated falls. RESULTS: When walking with MRBA, the healthy subjects have a lower speed, smaller step length and longer step time. The SCI subject experiences similar changes as well as a decrease in step width that indicates better stability. Both groups of subjects have reduced joint range of motion. By comparing the force sensor measurement with a calibrated threshold, the instability detection algorithm can identify more than 93% of self-induced falls with a false alarm rate of 0%. CONCLUSIONS: While there is still room for improvement in the robot compliance and the instability identification, the study demonstrates the first step in bringing gait assistive technologies into homes. We hope that the robot can encourage the balance-impaired population to engage in more activities of daily living to improve their quality of life. Future research includes recruiting more subjects with balance difficulty to further refine the device functionalities.

Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling.

Gait evaluation is important in gait rehabilitation and assistance to monitor patient's balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user's lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear.

Application of Signed Distance Function Neural Network in Real-Time Feet Tracking.

Loss of locomotion is one major problem faced by the elderly currently. Various rehabilitation technologies have been developed to assist them in recovering walking capability. Gait monitoring is an important aspect of lower-limb function rehabilitation. By observing the walking behaviour of a patient, the stability and recovery progress can be evaluated. Despite gait is often measured by motion capture and force-based measurement, these methods are costly and non-portable, whereas inertial measurement units (IMUs) require the attachment of sensors onto the subject's body. A few contactless measurements have been proposed, however, none of them views the feet from the back, making it non-trivial to transfer the method to over ground rehabilitation robots. This paper proposes a method to track the poses of the feet in real time using a novel deep neural network, termed SDF-Net, that models the signed distance function (SDF) of an object. Independent of the viewing angle, the algorithm receives the colour and depth images of the feet as input and computes the pose of the feet. The tracking accuracy is evaluated by having a subject to perform various actions with the feet; the dynamic errors are found to be less than 9 mm and 8 degrees for position and orientation errors respectively, which are better than the state-of-arts reviewed.