Heading constraint algorithm for foot-mounted PNS using low-cost IMU

Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.

An untethered cable-driven ankle exoskeleton with plantarflexion-dorsiflexion bidirectional movement assistance

Lower-limb assisted exoskeletons are widely researched for movement assistance or rehabilitation training.Due to advantages of compliance with human body and lightweight,some cable-driven prototypes have been developed,but most of these can assist only unidirectional movement.In this paper we present an untethered cable-driven ankle exoskeleton that can achieve plantarflexion-dorsiflexion bidirectional motion bilaterally using a pair of single motors.The main weights of the exoskeleton,i.e.,the motors,power supplement units,and control units,were placed close to the proximity of the human body,i.e.,the waist,to reduce the redundant rotation inertia which would apply on the wearer’s leg.A cable force transmission system based on gear-pulley assemblies was designed to transfer the power from the motor to the end-effector effectively.A cable self-tension device on the power output unit was designed to tension the cable during walking.The gait detection system based on a foot pressure sensor and an inertial measurement unit(IMU)could identify the gait cycle and gait states efficiently.To validate the power output performance of the exoskeleton,a torque tracking experiment was conducted.When the subject was wearing the exoskeleton with power on,the muscle activity of the soleus was reduced by 5.2%compared to the state without wearing the exoskeleton.This preliminarily verifies the positive assistance effect of our exoskeleton.The study in this paper demonstrates the promising application of a lightweight cable-driven exoskeleton on human motion augmentation or rehabilitation.

Design of the Pneumatic Pressure Smart Shoes for an Ankle-Assisted Exoskeleton

Ankle injury is one of the most common joint diseases that people experience during exercise.Most people have suffered ankle injuries at least once in their lives.The studies have shown that the ankle joint provides the most power and torques during the act of walking,compared to the knee and hip joints.This paper presents an ankle joint exoskeleton device,which is mainly used to provide assistance and protection to the human ankle joint with a pneumatic assist drive during walking.The pneumatic pressure smart shoes for this ankle exoskeleton were designed for detecting the human gaits to control the exoskeleton with certain supporting forces to the ankle joints at the appropriate timing.Each smart shoe has two sensors placed in between the wearable layer and the sole.The changes of the foot pressures were measured by the sensors for a microcontroller to control the exoskeleton.Two sets of experimental tests which were 2-month trials and gait selection were used to test the shoes.The experiments of 2-month trials were made to evaluate the stability of the shoes.The results showed that the shoes had no damages,no air leakage,and no malfunctions after the trials.The trials of gait selection were made to test the recognition rate which reached at 99.9%for the shoe system.The results showed that the design of the pneumatic smart shoes for the ankle-assisted exoskeleton met the requirements.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.