A real-time walking pattern recognition method for soft knee power assist wear

Abstract

Real-time recognition of walking-related activities is an important function that lower extremity assistive devices should possess. This article presents a real-time walking pattern recognition method for soft knee power assist wear. The recognition method employs the rotation angles of thighs and shanks as well as the knee joint angles collected by the inertial measurement units as input signals and adopts the rule-based classification algorithm to achieve the real-time recognition of three most common walking patterns, that is, level-ground walking, stair ascent, and stair descent. To evaluate the recognition performance, 18 subjects are recruited in the experiments. During the experiments, subjects wear the knee power assist wear and carry out a series of walking activities in an out-of-lab scenario. The results show that the average recognition accuracy of three walking patterns reaches 98.2%, and the average recognition delay of all transitions is slightly less than one step.

A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons

“…Based on Table 2 , the most investigated LMs are dynamic tasks only [ 2 , 4 , 5 , 8 , 10 , 13 , 30 , 31 , 32 ] (N = 11) and a combination between dynamic and static tasks [ 3 , 6 , 9 , 11 , 12 , 34 , 35 ] (N = 7). Among the dynamic tasks, six studies [ 4 , 13 , 31 , 33 , 34 , 35 ] classified continuous dynamic tasks, whereas only one study [ 2 ] focused on transitions.…”

“…Among the dynamic tasks, six studies [ 4 , 13 , 31 , 33 , 34 , 35 ] classified continuous dynamic tasks, whereas only one study [ 2 ] focused on transitions. Thirteen studies [ 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 30 , 32 , 34 , 35 ] explored continuous and discrete dynamic tasks together.…”

“…Concerning the dynamic LMs, the LW, SA, and SD tasks were the continuous dynamic tasks typically addressed, being explored in 17 studies [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 30 , 31 , 32 , 33 , 34 , 35 ], while the RA and RD tasks were investigated together with the LW, SA, and SD tasks in nine studies [ 4 , 8 , 11 , 12 , 13 , 30 , 31 , 32 , 35 ]. The LW-SA, SA-LW, LW-SD, and SD-LW transitions were addressed in eight studies [ 5 , 6 , 7 , 8 , 9 , 10 , 30 , 32 ], being the most investigated discrete dynamic tasks. The LW-RA, RA-LW, LW-RD, and RD-LW tasks were explored in four studies [ 8 , 9 , 30 , 32 ], the SIT-ST and ST-SIT in four studies [ 2 , 6 , 7 , 35 ], the ST-LW and LW-ST in two studies [ 6 , 7 ], and the ST-SA and SA-ST in one study [ 6 ].…”

“…Ten studies [ 3 , 5 , 6 , 7 , 8 , 11 , 12 , 13 , 32 , 33 ] presented information regarding the walking speed during the LW task, whereas only four studies presented its value (1.0 [ 33 ], 1.5 [ 33 ], 2.7 [ 11 , 12 ], and 4.0 km/h [ 13 ]). While in [ 13 , 33 ], the presented speed value was employed during the LW tasks, in [ 11 , 12 ], the speed of 2.7 km/h was enforced during the LW, RA, and RD tasks, using a treadmill.…”

“…Current challenges in personalized robotics-based assistance are related to recognizing and predicting different LMs with a non-intrusive sensor setup to timely trigger the assistance delivered by the wearable assistive devices. Despite recent advancements, most of the current LM decoding tools integrated into wearable assistive devices (i) address a limited number of daily LMs (are non-generic tools) [ 2 , 3 ]; (ii) present high recognition delays [ 4 , 5 , 6 , 7 ] and low prediction times [ 6 , 8 , 9 , 10 ]; (iii) do not consider the effect of the walking speed variation [ 6 , 8 , 11 , 12 ]; (iv) do not present clinical evidence [ 3 , 6 , 8 , 13 ]; and (v) do not adapt the assistance to perform the intended motion [ 3 , 6 , 7 , 8 , 13 ]. It is of the utmost importance that wearable assistive devices, such as AOs and exoskeletons, tackle these limitations by endowing algorithms capable of accurately and timely decoding of the user’s LMs to provide personalized assistance [ 7 , 14 ].…”