Bergmann Jeroen H M, Langdon Patrick M, Mayagoitia Ruth E, Howard Newton
Brain Sciences Foundation, Providence, Rhode Island, United States of America ; Centre of Human & Aerospace Physiological Sciences, King's College London, London, United Kingdom ; Synthetic Intelligence Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Department of Engineering, The University of Cambridge, Cambridge, United Kingdom.
PLoS One. 2014 Feb 7;9(2):e88080. doi: 10.1371/journal.pone.0088080. eCollection 2014.
Humans appear to be sensitive to relative small changes in their surroundings. These changes are often initially perceived as irrelevant, but they can cause significant changes in behavior. However, how exactly people's behavior changes is often hard to quantify. A reliable and valid tool is needed in order to address such a question, ideally measuring an important point of interaction, such as the hand. Wearable-body-sensor systems can be used to obtain valuable, behavioral information. These systems are particularly useful for assessing functional interactions that occur between the endpoints of the upper limbs and our surroundings. A new method is explored that consists of computing hand position using a wearable sensor system and validating it against a gold standard reference measurement (optical tracking device). Initial outcomes related well to the gold standard measurements (r = 0.81) showing an acceptable average root mean square error of 0.09 meters. Subsequently, the use of this approach was further investigated by measuring differences in motor behavior, in response to a changing environment. Three subjects were asked to perform a water pouring task with three slightly different containers. Wavelet analysis was introduced to assess how motor consistency was affected by these small environmental changes. Results showed that the behavioral motor adjustments to a variable environment could be assessed by applying wavelet coherence techniques. Applying these procedures in everyday life, combined with correct research methodologies, can assist in quantifying how environmental changes can cause alterations in our motor behavior.
人类似乎对周围环境中相对较小的变化很敏感。这些变化最初往往被认为无关紧要,但它们可能会导致行为上的显著变化。然而,人们的行为究竟是如何变化的,往往很难量化。为了解决这样一个问题,需要一个可靠且有效的工具,理想情况下是测量一个重要的交互点,比如手。可穿戴式身体传感器系统可用于获取有价值的行为信息。这些系统对于评估上肢端点与我们周围环境之间发生的功能交互特别有用。探索了一种新方法,该方法包括使用可穿戴传感器系统计算手部位置,并将其与金标准参考测量(光学跟踪设备)进行验证。初步结果与金标准测量结果相关性良好(r = 0.81),平均均方根误差为0.09米,可接受。随后,通过测量运动行为的差异,进一步研究了这种方法在应对不断变化的环境时的应用。三名受试者被要求用三个略有不同的容器执行倒水任务。引入小波分析来评估这些微小的环境变化如何影响运动一致性。结果表明,应用小波相干技术可以评估对可变环境的行为运动调整。在日常生活中应用这些程序,并结合正确的研究方法,可以帮助量化环境变化如何导致我们运动行为的改变。