Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines

Abstract

As wearable sensors are becoming more common, their utilization in real-world applications is also becoming more attractive. In this study, a single wrist-worn inertial measurement unit was attached to the active wrist of a worker and acceleration and angular speed information was used to decide what activity the worker was performing at certain time intervals. This activity information can then be used for proactive instruction systems or to ensure that all the needed work phases are performed. In this study, the selected activities were basic tasks of hammering, screwing, spanner use and using a power drill for screwing. In addition, a null activity class consisting of other activities (moving around the post, staying still, changing tools) was defined. The performed activity could then be recognized online by using a sliding window method to divide the data into two-second intervals and overlapping two adjacent windows by 1.5 seconds. Thus, the activity was recognized every half second. The method used for the actual recognition was the k nearest neighbor method with a specific distance boundary for classifying completely new events as null data. In addition, the final class was decided by using a majority vote to classifications of three adjacent windows. The results showed that almost 90 percent accuracy can be achieved with this kind of setting; the activity-specific accuracies for hammering, screwing, spanner use, power drilling and null data were 96.4%, 89.7%, 89.5%, 77.6% and 89.0%, respectively. In addition, in a case with completely new null events, use of the specific distance measure improved accuracy from 68.6% to 82.3%.

Human Activity Recognition Using Accelerometer and Gyroscope Sensors

“…It is discovered that exercises can be recognized by utilizing moderately few elements [4], [5], [9]. The studies have also been performed to see if using more components will produce better results [10], [11]. Table I display a comparative analysis of the earlier works performed, for detecting activities using phone sensors.…”

Automatic Measurement of Lead Time of Repetitive Assembly Work in a Factory Using a Wearable Sensor

“…Koskimäki et al [22] obtain acceleration and gyro sensor data from a wrist-worn inertial sensor device and analyze operation processes in a line production system to ensure that all necessary operations are performed. The study recognizes such activities as hammering and screwing by using kNN search.…”

Task recognition from joint tracking data in an operational manufacturing cell

“…While the latter can allow for very precise control of experimental variables, it misses both the variance that occurs naturally in human tasks and the subtle changes in performance or behavior that occur when workers are being actively or passively critiqued by their observers. In the manufacturing literature on activity recognition (Chen et al 2015;Huikari et al 2010;Koskimaki et al 2009;Stiefmeier et al 2006;Ward et al 2006), data were collected in a simulated environment and not at an in-production facility. Also, these studies used on-body sensors-and, in some cases, other sensors such as microphones-to collect data on the worker.…”