Development of a Wearable IMU System for Automatically Assessing Lifting Risk Factors

Wearable Devices for Ergonomics: A Systematic Literature Review

“…This confirms that “the real-time assessment of human movements and posture through wearable sensors can inform workers about inadequate lifting postures, dramatically helping in preventing injury risks” [ 42 ]. Such results also emphasise the statement by Lu et al [ 57 ], according to which “the application of IMUs for tracking human motion as a part of the ergonomic assessment is becoming popular because the collection of the human body motion does not greatly interrupt with workers’ job performance”.…”

“…All the sensor systems, with the exception of those described in [ 50 ] and [ 58 ], are based on IMUs. Such component combines information obtained from multiple electromechanical sensors (e.g., accelerometers, gyroscopes, and magnetometers) [ 57 ], and is self-contained and unobtrusive [ 44 ]. In accordance with Cerqueira et al [ 48 ], “regarding the positioning of the IMUs on the human body, there is no standard established protocol and each author proposes different locations for the sensors”.…”

“…Other methods listed in the ISO 11228-3 [ 68 ] are used by the studies (e.g., Ovako Working Posture Analysis System, Rapid Entire Body Assessment, ACGIH TLV for lifting), but only one [ 55 ] employs the Occupational Repetitive Actions (OCRA) Index, which is the preferred method for detailed risk assessments. To assess manual lifting tasks, 3 studies contained in 4 papers [ 13 , 42 , 52 , 57 ] take into consideration the revised NIOSH lifting equation, quoting the applications manual NIOSH 94-110 [ 69 ] and the International Standard ISO 11228-1 [ 70 ].…”

“…For instance, the shoulder complex is typically monitored by only one IMU placed in the upper arm, while the neck by another one located in the forehead or vertebrae C4. According to Lu et al [ 57 ], although generally detailed whole-body biomechanical models to track body motion typically require from 13 to 17 IMUs, a configuration based on five IMUs may adequately reconstruct the whole-body posture to discriminate gross movement activities. In addition to the number of sensors, another relevant parameter to be taken into account during the design stage concerns their location.…”

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

“…Moreover, machine learning (ML) algorithms are gaining popularity in the ergonomic field for biomechanical risk assessment by means of data acquired by wearable inertial systems. Several publications have appeared in recent years documenting several strategies [ 27 , 28 , 29 ]. IMU systems, which incorporate machine learning into their data analysis pathways, have been found effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises, including lifting, despite studies in this field having so far involved few samples [ 30 ].…”