Exploring the Use of Sensors to Measure Behavioral Interactions: An Experimental Evaluation of Using Hand Trajectories

• Published in: PloS one Vol. 9; no. 2; p. e88080
• Main Authors: Bergmann, Jeroen H. M., Langdon, Patrick M., Mayagoitia, Ruth E., Howard, Newton
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.02.2014; Public Library of Science (PLoS)
• Subjects: Adult; Analysis; Autism; Biology; Brain research; Containers; Engineering; Environmental changes; Female; Hand - physiology; Human behavior; Human mechanics; Humans; Information systems; Laboratories; Measurement; Measuring instruments; Medicine; Monitoring, Physiologic; Motor Activity - physiology; Movement - physiology; Optical tracking; Personality; Position sensing; Sensors; Social and Behavioral Sciences; Tracking devices; Tracking equipment; Trajectory analysis; Validity; Wavelet analysis; Wearable technology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0088080

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.