Intuitive Control of a Hybrid Prosthetic Leg During Ambulation

混合假肢在行走过程中的直观控制

• 批准号: 10456766
• 负责人: Levi John Hargrove
• 金额: $ 55.48万
• 依托单位: REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-06 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456766
• 关键词: Accelerometer; Algorithms; American; Amputation; Amputees; Ankle; Architecture; Artificial Leg; Award; Biomechanics; Car Phone; Cellular Phone; Classification; Clinic; Clinical; Communities; Community Participation; Custom; Data; Data Set; Development; Devices; Employment; Enrollment; Environment; Funding; Gait; Goals; Home; Home environment; Hybrids; Individual; Intuition; Knee; Knee Prosthesis; Laboratories; Learning; Leg; Leisures; Lower Extremity; Manuals; Monitor; Motor; Outcome; Participant; Patients; Pattern Recognition; Phase; Prosthesis; Protocols documentation; Quality of life; Ramp; Randomized; Randomized Clinical Trials; Research; Safety; Signal Transduction; System; Technology; Testing; Torque; Training; Walking; Work; base; biomechanical test; design; experience; falls; foot; improved; improved mobility; innovation; light weight; limb amputation; mobile application; novel; powered prosthesis; prosthesis control; psychologic; secondary outcome; sensor; transmission process; week trial

Project Summary/Abstract Most individuals with lower limb amputations use passive prostheses, which do not provide energy to assist with activities such as stair or ramp ascent or sit-to-stand transitions. This limits mobility, in particular for those with above-knee amputations. Powered leg prostheses could improve the mobility and community participation of such individuals; however, these devices are heavy, and current control systems require the user to manually transition between different ambulation activities, which is cumbersome. With prior R01 funding, we developed an adaptive, hierarchical pattern recognition control system that uses data from sensors on the prosthesis, and incorporates electromyographic (EMG) signals from the user, depending on their reliability, to determine user intent and enable safe prosthesis control with automatic, seamless transitions between ambulation activities. A mobile application allows for rapid tuning of the prosthesis and enables the user to choose between manual or automatic transitions. With other funding, we developed a novel prosthetic leg that can operate in passive mode —during level-ground walking or in active mode—during activities such as stair climbing or sit-to-stand transitions. This approach enables smaller, lighter motors, transmissions, and batteries, making our Hybrid Leg significantly lighter and quieter than other powered devices. Our long-term objective is to develop clinically viable technologies to improve the quality of life for lower limb amputees. A lightweight powered prosthesis with a safe, intuitive control system may increase mobility—facilitating employment, leisure, and community participation activities—and reduce the physical and psychological consequences of low activity. We will compare the Hybrid Leg with subjects' passive devices in both in-lab and home environments. In Aim 1, we will transition our adaptive control system to the Hybrid Leg, train users to walk with this device while the experimenter manually transitions the device between activity modes, and collect sensor data and EMG signals to create a user-specific pattern recognition control system. We will then determine the classification accuracy of this system. Aims 2 and 3 together constitute a randomized clinical trial, with AB-BA design, comparing the Hybrid leg with subjects' own passive devices. In Aim 2, we will provide advanced community-mobility training for either the subject's passive leg or the Hybrid leg, in random order, to meet both subject-specific and general activity goals necessary for community ambulation, and complete a full biomechanical assessment of ambulation activities such as stair or ramp ascent/descent and sit-to-stand transitions with that leg. In Aim 3, subjects will use the same leg for 4 weeks in their home and community, where activity and community participation will be monitored by a custom smartphone–based app. We will compare the number of steps taken and number of transitions between activities for each device. We expect that the control system will be safe, with a low classification error rate and without errors that may cause a fall. In addition, we hypothesize that, using the Hybrid leg, subjects will ambulate more and transition between activities more frequently, with biomechanics more similar to those of non-amputees.

项目总结/摘要 大多数下肢截肢者使用被动假肢，其不提供能量来帮助 活动，如楼梯或坡道上升或坐到站的过渡。这限制了流动性，特别是对于那些 膝盖以上截肢动力腿假肢可以改善残疾人的活动能力和社区参与， 然而，这些装置很重，并且当前的控制系统需要用户手动地 在不同的大使馆活动之间进行过渡，这是很麻烦的。在之前的R 01资助下，我们开发了 自适应分层模式识别控制系统，其使用来自所述假体上的传感器的数据，以及 结合来自用户的肌电图（EMG）信号，取决于其可靠性，以确定用户 意图和实现安全的假肢控制与自动，无缝过渡之间的amplitude活动。一 移动的应用允许假体的快速调整，并使用户能够在手动或手动之间进行选择。 自动转换。利用其他资金，我们开发了一种新型假肢，可以在被动模式下运行 - 在平地行走时或在活动模式下-在爬楼梯或坐立等活动中 过渡。这种方法可以实现更小，更轻的电机，变速器和电池，使我们的混合腿 比其他电动设备更轻、更安静。我们的长期目标是开发临床可行的 提高下肢截肢者生活质量的技术。一种轻便的动力假肢， 直观控制系统可以增加机动性，从而促进就业、休闲和社区参与 活动-并减少低活动的生理和心理后果。我们将比较混合动力车 在实验室和家庭环境中使用受试者的无源设备。在目标1中，我们将把我们的自适应 控制系统的混合腿，训练用户走这个设备，而实验者手动过渡 在活动模式之间切换设备，并收集传感器数据和EMG信号以创建用户特定模式 识别控制系统然后，我们将确定该系统的分类精度。目标2和3 一起构成了一项随机临床试验，采用AB-BA设计，比较混合腿与受试者自己的腿 无源器件在目标2中，我们将为受试者提供先进的社区活动训练， 腿或混合腿，以随机顺序，以满足特定学科和一般活动目标所需的 社区锻炼，并完成锻炼活动的全面生物力学评估，如楼梯或 斜坡上升/下降和坐到站的过渡与腿。在目标3中，受试者将使用同一条腿进行4次测试 在他们的家里和社区，活动和社区参与将由一个自定义监测周 基于智能手机的应用程序。我们将比较所采取的步骤数和活动之间的转换数 对于每个设备。我们期望控制系统将是安全的，具有低分类错误率并且没有 可能导致跌倒的错误。此外，我们假设，使用混合腿，受试者将更多地走动 更频繁地在活动之间转换，生物力学与非截肢者更相似。

项目成果

User intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis.

• DOI: 10.1109/embc.2016.7592194
• 发表时间: 2016-08
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Woodward RB;Spanias JA;Hargrove LJ
• 通讯作者: Hargrove LJ

Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs.

• DOI: 10.1186/s12984-023-01232-6
• 发表时间: 2023-09-04
• 期刊: JOURNAL OF NEUROENGINEERING AND REHABILITATION
• 影响因子: 5.1
• 作者: Kim, Minjae;Hargrove, Levi J.
• 通讯作者: Hargrove, Levi J.

Targeted Muscle Reinnervation for the Upper and Lower Extremity.

• DOI: 10.1097/bto.0000000000000194
• 发表时间: 2017-06
• 期刊: Techniques in orthopaedics (Rockville, Md.)
• 作者: Kuiken TA;Barlow AK;Hargrove L;Dumanian GA
• 通讯作者: Dumanian GA

Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.

• DOI: 10.1109/tmrb.2023.3282325
• 发表时间: 2023-08
• 期刊: IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS
• 作者: Ahkami, Bahareh;Ahmed, Kirstin;Thesleff, Alexander;Hargrove, Levi;Ortiz-Catalan, Max
• 通讯作者: Ortiz-Catalan, Max

Deep generative models with data augmentation to learn robust representations of movement intention for powered leg prostheses.

• DOI: 10.1109/tmrb.2019.2952148
• 发表时间: 2019-11
• 期刊: IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS
• 作者: Hu, Blair;Simon, Ann M.;Hargrove, Levi
• 通讯作者: Hargrove, Levi