Rebecca Charles

How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection

Human visual inspection skills remain superior for ensuring product quality and conformance to standards in the manufacturing industry. However, at present these skills cannot be formally shared with other workers or used to develop and implement new solutions or assistive technologies because they involve a high level of tacit knowledge which only exists in skilled operators internal cognitions. Industry needs reliable methods for the capture and analysis of this tacit knowledge so that it can be shared and not lost but also so that it can be best utilised in the transfer of manual work to automated systems and introduction of new technologies and processes. This paper describes two UK manufacturing case studies that applied systematic task analysis methods to capture and scrutinise the tacit knowledge and skills being applied in the visual inspection of aerospace components. Results reveal that the method was effective in eliciting tacit knowledge, and showed that tacit skills are particularly needed when visual inspection standards lack specification or the task requires greater subjective interpretation. The implications of these findings for future research and for developments in the manufacturing industry are discussed.

Understanding the human performance envelope using electrophysiological measures from wearable technology

In this article, we capture electrophysiological measures from a new wearable technology to understand the human performance envelope. Using the NASA Multi-Attribute Task Batteryxa0(MATB II), participants completed tasks associated with flight control which included communication, tracking and system and resource monitoring. Electrophysiological measures relating to cardiac activity and respiration were taken using the new wearable technology. Our results show significant differences in both heart rate and respiration rate in response to different taskloads and that higher taskloads were associated with higher mental workload. Frequency measures of heart rate variability discriminated different task types but not taskloads. This finding may be related to differences in task complexity being more important than the number events which we have used to manipulate taskload. We suggest that this new generation of wearable sensors could be used to inform operator locus in a human performance envelope, indicating when assistance by the aircraft or another crew member may be necessary to maintain safe and efficient performance.

Measuring mental workload using physiological measures: A systematic review

Technological advances have led to physiological measurement being increasingly used to measure and predict operator states. Mental workload (MWL) in particular has been characterised using a variety of physiological sensor data. This systematic review contributes a synthesis of the literature summarising key findings to assist practitioners to select measures for use in evaluation of MWL. We also describe limitations of the methods to assist selection when being deployed in applied or laboratory settings. We detail fifty-eight peer reviewed journal articles which present original data using physiological measures to include electrocardiographic, respiratory, dermal, blood pressure and ocular. Electroencephalographic measures have been included if they are presented with another measure to constrain scope. The literature reviewed covers a range of applied and experimental studies across various domains, safety-critical applications being highly represented in the sample of applied literature reviewed. We present a summary of the six measures and provide an evidence base which includes how to deploy each measure, and characteristics that can affect or preclude the use of a measure in research. Measures can be used to discriminate differences in MWL caused by task type, task load, and in some cases task difficulty. Varying ranges of sensitivity to sudden or gradual changes in taskload are also evident across the six measures. We conclude that there is no single measure that clearly discriminates mental workload but there is a growing empirical basis with which to inform both science and practice.