Analysis and Suppression of Tremor During Grasp Using Ultrasound Imaging and Electrical Stimulation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 12
- 试验地点
- 2
- 主要终点
- Ultrasound Imaging Based Frequency Detection
研究概览
简要总结
Individuals experiencing tremors face difficulty performing activities of daily living caused by involuntary oscillation of the muscles in the hands and arms. Current solutions to help suppress tremors include medication, surgery, assistive devices and lifestyle change. However, each of these has a drawback of its own including cost and unwanted side effects. Aside from the solutions listed, it has been shown that functional electrical stimulation (FES) is a possible solution to help suppress tremor. Additionally, FES can be combined with different technologies including accelerometers, gyroscopes and motion capture to develop a closed loop system for tremor suppression. However, this has drawbacks including signal interference and the need for multiple sensor to fully classify the tremor. Ultrasound imaging solves some of these issues because it can provide a direct visualization of hand muscles that contribute to tremor. This study will focus on detecting characterizing and differentiating tremors from voluntary hand motion using ultrasound imaging. The results obtained from this study will help design FES-based tremor-suppression techniques in the future. This study will target both subjects with different tremor disorders and able bodied subjects.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
Ultrasound Imaging Based Frequency Detection
时间窗: Through Completion of Study, an average of 3 years
The investigators will benchmark the performance of the ultrasound features (fascicle length \[mm\], muscle thickness \[mm\]) in detecting the actual tremor frequency (Hz) measured by an inertial measurement unit (IMU) sensor and electromyography (EMG). Here error between the tremor frequency measured with IMU and the ultrasound feature-derived tremor frequency is reported. A lower mean error (0-100%) implies higher concurrence with the IMU-derived or EMG-derived tremor frequency.
Tremor Model Accuracy
时间窗: Through Completion of Study, an average of 3 years
The investigators will use ultrasound features collected on participants to develop novel models for tremor that can accurately predict the joint position. The joint position measured by the IMU will used as a benchmark and the root mean squared error (RMSE \[Deg.\]) between the model's predicted joint angle and the true angle will be used as a metric. A higher RMSE (in the scale of 0-100%) implies lower model accuracy.
Ultrasound Imaging Based Frequency Detection
时间窗: Through Completion of Study, an average of 3 years
The investigators will benchmark the performance of the ultrasound features (fascicle length \[mm\], muscle thickness \[mm\]) in detecting the actual tremor frequency (Hz) measured by an inertial measurement unit (IMU) sensor and electromyography (EMG). Here error between the tremor frequency measured with IMU and the ultrasound feature-derived tremor frequency is reported. A lower mean error (0-100%) implies higher concurrence with the IMU-derived or EMG-derived tremor frequency.
Tremor Suppression (Percentage)
时间窗: Through Completion of Study, an average of 3 years
The investigators will use the developed tremor models to develop closed loop control methods using FES which enable tremor suppression. The goal of the controller will be to maintain a desired wrist angle position while performing a grasping motion. A tremor suppression ratio metric, formulated as 1-(W\_t/W\_b), where W\_t is the angular velocity of the wrist around the vertical during periods in which stimulation was turned on and W\_b is the angular velocity during baseline periods without any stimulation, is used as an evaluation metric. A higher tremor suppression ratio (0-100%) means higher effectiveness in suppressing the tremor.
次要结局
未报告次要终点
研究者
Nitin Sharma
Associate Professor
North Carolina State University