Trunk kinematics data for balance disturbances and activities of daily living
收藏DataCite Commons2024-12-17 更新2024-11-06 收录
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https://figshare.com/articles/dataset/Trunk_kinematics_data_for_balance_disturbances_and_activities_of_daily_living/25569873/2
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This trunk kinematics dataset contains experimental data from six young and healthy adults for typical daily activities and trip- and slip-like perturbed locomotion that were measured by a self-developed wearable sensor, the Inertial measurement cluster (IMC). The sensor directly measures and records the angular velocity and the angular acceleration vector of the rigid body to which it is attached.In general, remote monitoring and evaluation of human motion during daily life requires accurate extraction of kinematic quantities of body segments. The perturbation were applied with highly standardized mechanical mechanisms. Current approaches use inertial sensors that require numerical time differentiation to access the angular acceleration vector, a mathematical operation that greatly increases the noise in the acceleration value. IIn this research, we use the IMC to directly measure the angular velocity and acceleration vector of the trunk during perturbed locomotion. As an application, we demonstrated a framework for automatically detecting and classifying the type of balance perturbation (tripping vs. slipping vs. forward loss of balance vs. backward loss of balance during stance) outside the laboratory.<br>Abbreviations:SLOW: slow gait velocity; PRF: preferred gait velocity; FAST: fast gait velocityPERT: perturbed walking; LRT_FW: Lean-and-release task forward directed; UNPERT: unperturbed walkingPAD: Pick up and drop; SC: Staircase walking; TUG: Time up and go; REST: quiet stance
本躯干运动学数据集收录了6名健康青年受试者在典型日常活动、类绊倒与类滑倒扰动行走场景下的实验数据,相关数据由自研可穿戴传感器——惯性测量簇(Inertial measurement cluster, IMC)采集获取。该传感器可直接测量并记录其附着刚体的角速度与角加速度矢量。
一般而言,开展日常生活中人体运动的远程监测与评估,需精准提取人体各节段的运动学参数。本次实验采用高度标准化的机械装置施加运动扰动。当前主流方案多采用惯性传感器,需通过数值时间微分获取角加速度矢量,该数学操作会大幅放大加速度数据中的噪声。
本研究中,我们借助IMC直接采集扰动行走过程中躯干的角速度与加速度矢量。作为应用案例,我们搭建了一套可在实验室外自动检测并分类平衡扰动类型(绊倒、滑倒、站立时向前失稳、站立时向后失稳)的框架。
缩写对照表:
SLOW:慢速步态;PRF:首选步态速度;FAST:快速步态;
PERT:扰动行走;LRT_FW:前向倾靠释放任务;UNPERT:无扰动行走;
PAD:拾取与放置;SC:楼梯行走;TUG:计时起立-行走测试;REST:静立姿态
提供机构:
figshare
创建时间:
2024-09-29



