Weston Angela R, Loyd Brian J, Taylor Carolyn, Hoppes Carrie, Dibble Leland E
Department of Physical Therapy and Athletic Training, University of Utah, 520 Wakara Way, Salt Lake City, UT 84108, USA.
Department of Physical Therapy and Rehabilitation Sciences, University of Montana, 32 Campus Dr., Missoula, MT 59812, USA.
Sensors (Basel). 2022 Apr 16;22(8):3071. doi: 10.3390/s22083071.
Alterations in head and trunk kinematics during activities of daily living can be difficult to recognize and quantify with visual observation. Incorporating wearable sensors allows for accurate and measurable assessment of movement. The aim of this study was to determine the ability of wearable sensors and data processing algorithms to discern motion restrictions during activities of daily living. Accelerometer data was collected with wearable sensors from 10 healthy adults (age 39.5 ± 12.47) as they performed daily living simulated tasks: coin pick up (pitch plane task), don/doff jacket (yaw plane task), self-paced community ambulation task [CAT] (pitch and yaw plane task) without and with a rigid cervical collar. Paired t-tests were used to discern differences between non-restricted (no collared) performance and restricted (collared) performance of tasks. Significant differences in head rotational velocity (jacket p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), head rotational amplitude (coin p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), trunk rotational amplitude (jacket p = 0.01, CAT-yaw p = 0.005), and head−trunk coupling (jacket p = 0.007, CAT-yaw p = 0.003) were captured by wearable sensors between the two conditions. Alterations in turning movement were detected at the head and trunk during daily living tasks. These results support the ecological validity of using wearable sensors to quantify movement alterations during real-world scenarios.
在日常生活活动中，头部和躯干的运动学变化很难通过视觉观察来识别和量化。使用可穿戴传感器能够对运动进行准确且可测量的评估。本研究的目的是确定可穿戴传感器和数据处理算法在识别日常生活活动中的运动限制方面的能力。通过可穿戴传感器收集了10名健康成年人（年龄39.5±12.47岁）在执行日常生活模拟任务时的加速度计数据：捡硬币（俯仰平面任务）、穿脱夹克（偏航平面任务）、自主节奏的社区行走任务[CAT]（俯仰和偏航平面任务），分别在不戴和戴着硬质颈托的情况下进行。采用配对t检验来辨别任务在无限制（不戴颈托）表现和受限（戴颈托）表现之间的差异。可穿戴传感器捕捉到了两种情况下头部旋转速度（穿脱夹克p = 0.03，CAT - 俯仰p < 0.001，CAT - 偏航p < 0.001）、头部旋转幅度（捡硬币p = 0.03，CAT - 俯仰p < 0.001，CAT - 偏航p < 0.001）、躯干旋转幅度（穿脱夹克p = 0.01，CAT - 偏航p = 0.005）以及头部 - 躯干耦合（穿脱夹克p = 0.007，CAT - 偏航p = 0.003）的显著差异。在日常生活任务中，检测到头部和躯干在转身运动方面的变化。这些结果支持了使用可穿戴传感器在现实场景中量化运动变化的生态效度
