Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Mayo Clinic, Rochester, MN 55905, USA; Division of Health Care Policy and Research, Department of Health Sciences Research Mayo Clinic, Rochester, MN 55905, USA.
Program in Physical Therapy, Mayo Clinic School of Health Sciences Mayo Clinic, Rochester, MN 55905, USA; Assistive and Restorative Technology Laboratory, Rehabilitation Medicine Research Center, Department of Physical Medicine and Rehabilitation Mayo Clinic, Rochester, MN 55905, USA.
J Electromyogr Kinesiol. 2022 Feb;62:102337. doi: 10.1016/j.jelekin.2019.07.007. Epub 2019 Jul 17.
Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants' free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants' estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users' field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.
肩部疼痛在手动轮椅（MWC）使用者中很常见。过度使用被认为是主要原因，但对于日常生活活动（ADL）中的暴露情况知之甚少。该研究的目的是开发一种方法，以便在现场估计三种情况：（1）非推进活动，（2）MWC 推进，以及（3）使用惯性测量单元（IMU）的静态时间。上肢 IMU 数据是在十位 MWC 用户进行基于实验室的 MWC 相关 ADL 时收集的。开发了一种神经网络模型，用于将数据分类为非推进活动、推进或静态，并通过视频比较对基于实验室的数据收集进行验证。收集了六位参与者的自由生活 IMU 数据，并将基于实验室的模型应用于估计日常非推进活动、推进和静态时间。神经网络模型在区分非推进活动、推进和静态时间方面产生的实验室有效性度量值≥0.87。对一名参与者的现场数据进行的准验证得出，识别推进的有效性度量值≥0.66。参与者估计的日常非推进活动、推进和静态时间的平均值范围分别为 158 至 409 分钟、13 至 25 分钟和 367 至 609 分钟。初步结果表明，该模型可能能够准确识别 MWC 用户的现场活动。将现场的 IMU 数据纳入模型中可以进一步提高现场分类的准确性。
