Future-Shape GmbH, Altlaufstraße 34, 85635, Höhenkirchen-Siegertsbrunn, Germany.
Neuroscientific System Theory (NST), Department of Electrical Engineering and Information Technology, Technische Universität München (TUM), Karlstraße 45, 80333 München, Germany.
Comput Biol Med. 2018 Apr 1;95:271-276. doi: 10.1016/j.compbiomed.2017.11.003. Epub 2017 Nov 7.
Ageing has an effect on many parameters of the physical condition, and one of them is the way a person walks. This property, the gait pattern, can unintrusively be observed by letting people walk over a sensor floor. The electric capacitance sensors built into the floor deliver information about when and where feet get into close proximity and contact with the floor during the phases of human locomotion. We processed gait patterns recorded this way by extracting a feature vector containing the discretised distribution of occurring geometrical extents of significant sensor readings. This kind of feature vector is an implicit measure encoding the ratio of swing-to stance phase timings in the gait cycle and representing how cleanly the leg swing is performed. We then used the dataset to train a Multi-Layer Perceptron to perform regression with the age of the person as the target value, and the feature vector as input. With this method and a dataset size of 142 persons recorded, we achieved a mean absolute error of approximately 10 years between the true age and the estimated age of the person. Considering the novelty of our approach, this is an acceptable result. The combination of a floor sensor and machine learning methods for interpreting the sensor data seems promising for further research and applications in care and medicine.
衰老是许多身体状况参数的影响因素之一，其中之一就是人的行走方式。这种特性，即步态模式，可以通过让人们在传感器地板上行走来进行非侵入式的观察。地板内置的电容传感器提供了关于在人体运动的各个阶段何时以及何处脚部与地板近距离接触和接触的信息。我们通过提取一个特征向量来处理以这种方式记录的步态模式，该特征向量包含发生的传感器读数的离散分布的几何范围。这种特征向量是一种隐式度量，编码步态周期中摆动到站立阶段时间的比例，并表示腿部摆动执行的干净程度。然后，我们使用数据集训练一个多层感知机，以年龄作为目标值，特征向量作为输入进行回归。使用这种方法和记录的 142 个人的数据集，我们在真实年龄和估计年龄之间的平均绝对误差约为 10 岁。考虑到我们方法的新颖性，这是一个可以接受的结果。地板传感器和机器学习方法相结合来解释传感器数据，这似乎是护理和医学领域进一步研究和应用的有前途的方法。
