Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks

Abstract

Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial–temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user’s gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks

“…We imported the A-mode ultrasound data and the joint kinematics data into MATLAB (Mathworks, Natick, MA, USA) and performed feature reduction as described in our previous lower-limb work [17][18] and previous upper-limb studies [50] (Fig. 3).…”

“…Researchers have proposed using high-level control strategies aiming to classify the user's intended ambulation mode online. These classifiers are typically based on onboard mechanical sensors [13] [14], such as inertial measurement units (IMU) and load cells, or a combination of mechanical sensors and neuromuscular signals, such as electromyography [15] [16] and sonomyography [17] [18]. The application of vision and depth sensing have also resulted in improved environment and ambulation mode classification [19] [20] [21].…”

“…In transradial amputees, Amode ultrasound has shown success in gesture recognition and wrist rotation estimation [46]. Among transfemoral amputees, A-mode ultrasound has been used for continuous ambulation mode classification [17] and for kinematic prediction during level ground walking [18]. These prior works illustrate the promise of the A-mode ultrasound sensing modality, but in order to translate the technology to everyday use applications, it must also be able to recognize the user's intent across a range of activities.…”

Biomechanics of Ascending and Descending Stairs in a Patient with Transfemoral Amputation and Neural Sensory Feedback: A Case Report

“…Gait impairment resulting from amputation has been objectively documented across various domains, including spatiotemporal and biomechanical parameters [10,11], as well as bioenergetics parameters. Individuals with transfemoral amputation, particularly those with dysvascular morbidity as the underlying cause, walk slower by 40% than normal, consume 2.5 times more energy [12], have increased oxygen consumption by about 20% compared to a healthy person [13], and face limitations in walking longer distances outdoors [14].…”

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network

“…Multiple able-bodied studies have applied the technology to gesture recognition [44], force estimation [45], and wrist/hand kinematics estimation [46]. Furthermore, Amode has allowed for finger gesture recognition and wrist rotation estimation with transradial amputee subjects [47], as well as ambulation mode recognition in above-knee amputee subjects [48]. However, it is not known whether ultrasound can be used to predict prosthesis kinematics in above-knee amputees.…”