Posture and motion variability in non-repetitive manual materials handling tasks

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer

OBJECTIVE

We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU).

BACKGROUND

Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety.

METHODS

Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision.

RESULTS

The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively.

CONCLUSION

The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Consistent individual motor variability traits demonstrated by females performing a long-cycle assembly task under conditions differing in temporal organisation

Research suggests an association between motor variability (MV) during repetitive work and work-related musculoskeletal disorders (MSDs). However, whether MV is a consistent individual trait, even across working conditions or tasks, remains unknown. This study assessed whether individual MV traits were consistent during complex work performed under different temporal conditions. Fifteen women performed cyclic assembly under four conditions differing in pace and organisation (line-type, batch-type). MV of trapezius muscle activity and upper arm elevation was quantified and partitioned into variance components. For all MV metrics, a non-zero between-subjects variance was found, indicating consistent individual MV traits across conditions. Variance between subjects was higher for electromyography (EMG) MV metrics compared with kinematic metrics. Our results showed individuals exhibited consistent MV traits across working conditions differing in pace and production process. Further research is needed to understand whether MV is an individual predictive factor for MSD onset or progression.

Human movement and ergonomics: An industry-oriented dataset for collaborative robotics

Improving work conditions in industry is a major challenge that can be addressed with new emerging technologies such as collaborative robots. Machine learning techniques can improve the performance of those robots, by endowing them with a degree of awareness of the human state and ergonomics condition. The availability of appropriate datasets to learn models and test prediction and control algorithms, however, remains an issue. This article presents a dataset of human motions in industry-like activities, fully labeled according to the ergonomics assessment worksheet EAWS, widely used in industries such as car manufacturing. Thirteen participants performed several series of activities, such as screwing and manipulating loads under different conditions, resulting in more than 5 hours of data. The dataset contains the participants’ whole-body kinematics recorded both with wearable inertial sensors and marker-based optical motion capture, finger pressure force, video recordings, and annotations by three independent annotators of the performed action and the adopted posture following the EAWS postural grid. Sensor data are available in different formats to facilitate their reuse. The dataset is intended for use by researchers developing algorithms for classifying, predicting, or evaluating human motion in industrial settings, as well as researchers developing collaborative robotics solutions that aim at improving the workers’ ergonomics. The annotation of the whole dataset following an ergonomics standard makes it valuable for ergonomics-related applications, but we expect its use to be broader in the robotics, machine learning, and human movement communities.

Relationship between motor variability, accuracy, and ball speed in the tennis serve

The main objective of this study was to analyze the motor variability in the performance of the tennis serve and its relationship to performance outcome. Seventeen male tennis players took part in the research, and they performed 20 serves. Linear and non-linear variability during the hand movement was measured by 3D Motion Tracking. Ball speed was recorded with a sports radar gun and the ball bounces were video recorded to calculate accuracy. The results showed a relationship between the amount of variability and its non-linear structure found in performance of movement and the outcome of the serve. The study also found that movement predictability correlates with performance. An increase in the amount of movement variability could affect the tennis serve performance in a negative way by reducing speed and accuracy of the ball.

Motor variability in occupational health and performance

Several recent reviews have reported that 'repetitive movements' constitute a risk factor for occupational musculoskeletal disorders in the neck, shoulder and arm regions. More variation in biomechanical exposure is often suggested as an effective intervention in such settings. Since increasing variation using extrinsic methods like job rotation may not always be possible in an industrial context, the intrinsic variability of the motor system may offer an alternative opportunity to increase variation. Motor variability refers to the natural variation in postures, movements and muscle activity observed to different extents in all tasks. The current review discusses research appearing in motor control, sports sciences and occupational biomechanics literature to answer whether motor variability is important to consider in an occupational context, and if yes, whether it can be manipulated by training the worker or changing the working conditions so as to increase biomechanical variation without jeopardizing production. The review concludes that motor variability is, indeed, a relevant issue in occupational health and performance and suggests a number of key issues for further research.

A neural network model for predicting postures during non-repetitive manual materials handling tasks

Posture prediction can be useful in facilitating the design and evaluation processes for manual materials handling tasks. This study evaluates the ability of artificial neural network models to predict initial and final lifting postures in 2-D and 3-D scenarios. Descriptors for the participant and condition of interest were input to the models; outputs consisted of posture-defining joint angles. Models were trained with subsets of an existing posture database before predictions were generated. Trained models predictions were then evaluated using the remaining data, which included conditions not presented during training. Prediction errors were consistent across these data subsets, suggesting the models generalised well to novel conditions. The models generally predicted whole-body postures with per-joint errors in the 5 degrees -20 degrees range, though some errors were larger, particularly for 3-D conditions. These models provided reasonably accurate predictions, even outperforming some computational approaches previously proposed for similar purposes. Suggestions for future refinement of such models are presented. The models in this investigation provide a means to predict initial and final postures in commonly occurring manual materials handling tasks. In addition, the model structures provide information about potential lifting strategies that may be used by individuals with particular anthropometry or strength characteristics.