Study on the Effects of Different Seat and Leg Support Conditions of a Trunk Rehabilitation Robot

Abstract

Performance of trunk rehabilitation exercises while sitting on movable surfaces with feet on the ground can increase trunk and leg muscle activations, and constraining the feet to move with the seat isolates control of the trunk. However, there are no detailed studies on the effects of these different leg supports on the trunk and leg muscle activations under unstable and forcefully perturbed seating conditions. We have recently devised a trunk rehabilitation robot that can generate unstable and forcefully perturbed sitting surfaces, and can be used with ground-mounted or seat-connected footrests. In this study, we have evaluated the differences in balance performance, trunk movement and muscle activation (trunk and legs) of fourteen healthy adults caused by the use of these different footrest configurations under the different seating scenarios. The center of pressure and trunk movement results show that the seatconnected footrest may be a more suitable choice for use in a balance recovery focused rehabilitation protocol, while the ground-mounted footrest may be a more suitable choice for a trunk movement focused rehabilitation protocol. Although it is difficult to make a clear selection between footrests due to the mixed trends observed in the muscle activation results, it appears that the seat-connected footrest may be preferable for use with the unstable seat as it causes greater muscle activations. Furthermore, the results provide limited evidence that targeting of a particular muscle group may be Manuscript

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and its Application in Trunk Rehabilitation

“…L stands for the loss function. Equation (1) implies that deep learning model f is optimized by minimizing the expected value of the loss between the output of the network f (X) and the ground truth label y.…”

“…Symbolˆdenotes the prediction value for the ground truth. Let ⃗ y = f (X) denote the output vector variable of the deep learning predictor ( ⃗ y = [ φ, μ, σ] = [ŷ (1) , ŷ(2) , ŷ(3) ]). y c is the ground truth for binary classification and is converted from y (the scalar random variable for label sequences):…”

A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and Its Application in Trunk Rehabilitation

“…Equation (1) implies that deep learning model f is optimized by minimizing the expected value of the loss between the output of the network f (X) and the ground truth label y.…”

“…Symbolˆdenotes the prediction value for the ground truth. Let ⃗ y = f (X) denote the output vector variable of the deep learning predictor ( ⃗ y = [ φ, μ, σ] = [ŷ (1) , ŷ(2) , ŷ(3) ]). y c is the ground truth for binary classification and is converted from y (the scalar variable for label sequences):…”

“…Dynamic seated postural control is the ability to maintain upright balance under gravity and internal/external perturbations during voluntary trunk movements [1]. It is the cornerstone to carrying out activities of daily living (ADL).…”