Department of Mechatronics Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea.
Sensors (Basel). 2021 Apr 15;21(8):2785. doi: 10.3390/s21082785.
The number of elderly people has increased as life expectancy increases. As muscle strength decreases with aging, it is easy to feel tired while walking, which is an activity of daily living (ADL), or suffer a fall accident. To compensate the walking problems, the terrain environment must be considered, and in this study, we developed the locomotion mode recognition (LMR) algorithm based on the gaussian mixture model (GMM) using inertial measurement unit (IMU) sensors to classify the five terrains (level walking, stair ascent/descent, ramp ascent/descent). In order to meet the walking conditions of the elderly people, the walking speed index from 20 to 89 years old was used, and the beats per minute (BPM) method was adopted considering the speed range for each age groups. The experiment was conducted with the assumption that the healthy people walked according to the BPM rhythm, and to apply the algorithm to the exoskeleton robot later, a full/individual dependent model was used by selecting a data collection method. Regarding the full dependent model as the representative model, the accuracy of classifying the stair terrains and level walking/ramp terrains is BPM 90: 98.74%, 95.78%, BPM 110: 99.33%, 95.75%, and BPM 130: 98.39%, 87.54%, respectively. The consumption times were 14.5, 21.1, and 14 ms according to BPM 90/110/130, respectively. LMR algorithm that satisfies the high classification accuracy according to walking speed has been developed. In the future, the LMR algorithm will be applied to the actual hip exoskeleton robot, and the gait phase estimation algorithm that estimates the user's gait intention is to be combined. Additionally, when a user wearing a hip exoskeleton robot walks, we will check whether the combined algorithm properly supports the muscle strength.
随着预期寿命的延长，老年人的数量增加了。随着年龄的增长，肌肉力量下降，在进行日常活动（ADL）步行时容易感到疲劳，或者发生跌倒事故。为了弥补步行问题，必须考虑地形环境，在本研究中，我们使用惯性测量单元（IMU）传感器基于高斯混合模型（GMM）开发了运动模式识别（LMR）算法，以对五种地形（水平行走、楼梯上下、斜坡上下）进行分类。为了满足老年人的步行条件，使用了 20 到 89 岁的步行速度指数，并考虑到每个年龄组的速度范围，采用了每分钟拍数（BPM）方法。实验假设健康人按照 BPM 节奏行走，为了以后将算法应用于外骨骼机器人，采用了全/个人依赖模型，通过选择数据收集方法来进行。作为代表性模型的全依赖模型，楼梯地形和平地/斜坡地形的分类准确率分别为 BPM90：98.74%、95.78%，BPM110：99.33%、95.75%，BPM130：98.39%、87.54%。根据 BPM90/110/130，分别需要 14.5、21.1 和 14 毫秒的时间。已经开发出了满足根据步行速度的高分类准确率的 LMR 算法。将来，LMR 算法将应用于实际的臀部外骨骼机器人，并结合估计用户步态意图的步态相位估计算法。此外，当佩戴臀部外骨骼机器人的用户行走时，我们将检查组合算法是否正确地支持肌肉力量。
