Passive derivation of basic walker-assisted gait characteristics from measured forces and moments

Gait Phase Estimation Based on User–Walker Interaction Force

Smart walkers have been developed for assistance and rehabilitation of elderly people and patients with physical health conditions. A force sensor mounted under the handle is widely used in smart walkers to establish a human–machine interface. The interaction force can be used to control the walker and estimate gait parameters using methods such as the Kalman filter for real-time estimation. However, the estimation performance decreases when the peaks of the interaction force are not captured. To improve the stability and accuracy of gait parameter estimation, we propose an online estimation method to continuously estimate the gait phase and cadence. A multiple model switching mechanism is introduced to improve the estimation performance when gait is asymmetric, and an adaptive rule is proposed to improve the estimation robustness and accuracy. Simulations and experiments demonstrate the effectiveness and accuracy of the proposed gait parameter estimation method. Here, the average estimation error for the gait phase is 0.691 rad when the gait is symmetric and 0.722 rad when it is asymmetric.

Use of Reaction Force to Evaluate Older Adults' Gait Patterns While Using a Walker to Walk

Walking is the most common activity in daily life. As people age, however, they begin to become imbalanced and need the assistance of mobility devices for walking, such as walkers. However, clinical gait measurement requires a lot of equipment to be worn; as walker users are seniors or disabled, this may cause them to be troubled in the assessment. Thus, this study used four load cells on the walker to estimate gait status. To understand the difference between the three groups of the Berg Balance Scale (BBS), 60 volunteers, who served as the subjects, were divided into three groups according to BBS scores, 20 volunteers for each group. Data were obtained from four load cells; walker users were divided per the BBS to observe their stance, swing phases, and support force while walking. The results of the study found that participants in the study were able to walk smoothly with the walker, and differences between the three groups in stance, swing phases, and support force were observed. The main findings of this study were: (1) While walking, the stance and swing phases could be stabilized by the evaluated gait; and (2) even if the user can walk stably, body function can be evaluated by the support force. We hope that our method will be widely applied in the design of mobility devices and in the evaluation of seniors’ care; we also hope our study will contribute to increasing knowledge, generally, in this field.

A New Controller for a Smart Walker Based on Human-Robot Formation

This paper presents the development of a smart walker that uses a formation controller in its displacements. Encoders, a laser range finder and ultrasound are the sensors used in the walker. The control actions are based on the user (human) location, who is the actual formation leader. There is neither a sensor attached to the user’s body nor force sensors attached to the arm supports of the walker, and thus, the control algorithm projects the measurements taken from the laser sensor into the user reference and, then, calculates the linear and angular walker’s velocity to keep the formation (distance and angle) in relation to the user. An algorithm was developed to detect the user’s legs, whose distances from the laser sensor provide the information necessary to the controller. The controller was theoretically analyzed regarding its stability, simulated and validated with real users, showing accurate performance in all experiments. In addition, safety rules are used to check both the user and the device conditions, in order to guarantee that the user will not have any risks when using the smart walker. The applicability of this device is for helping people with lower limb mobility impairments.

Smart walkers: an application-oriented review

In this paper, a development method for smart walker prototypes is proposed. Development of such prototypes is based on technological choices and device evaluations. The method is aimed at guiding technological choices in a modular fashion. First, the method for choosing modules to be integrated in a smart walker is presented. Application-specific modules are then studied. Finally, the issues of evaluation are investigated. In order to work out this method, more than 50 smart walkers and their pros and cons with respect to the different studied applications are reviewed.

Coordination of grasping and walking in Parkinson's disease

Studies on grasp control underlying manual dexterity in people with Parkinson disease (PD) suggest that anticipatory grasp control is mainly unaffected during discrete tasks using simple two-digit grasp. Nevertheless, impaired hand function during daily activities is one of the most disabling symptoms of PD. As many daily grasping activities occur during functional movements involving the whole body, impairments in anticipatory grasp control might emerge during a continuous dynamic task such as object transport during walking. In this case, grasp control must be coordinated along with multiple body segments. The present study investigated the effect of PD on anticipatory grasp control and intersegmental coordination during walking with a hand-held object. Nine individuals with idiopathic PD (tested OFF and ON medication) and nine healthy age-matched controls carried a grip instrument between their right thumb and index finger during self-paced and fast walking. Although the amplitude of grip forces was higher in standing and walking for subjects with PD, both subjects with PD and control subjects coupled grip and inertial force changes in an anticipatory fashion while walking. However, gait-induced motions of the object relative to that of the trunk (i.e., dampening) was reduced in subjects with PD. Medication increased the dampening in all subjects with PD. We suggest that these differences are associated with impairments in intersegmental coordination.