Table 1.

Instrument-based techniques designed on current technological advances and performing direct measurements by using sensors attached to the workers body. M and F indicate male and female respectively. IMUs: inertial measurement units; sEMG: surface electromyography.

Wearable Sensors	Author (Year)	Sample	Work Activity	Body Part Assessment	Aim	Findings	Quantitative Data
IMUs	Vignais et al. (2013) [84]	12 M	Manual tasks in an industrial environment	Upper body segment	Risk assessment of musculoskeletal disorders in real-time with two feedback	A real-time feedback significantly decreased the outcome of both globally as well as locally hazardous RULA values associated with increased risk for musculoskeletal disorders	Joint angle
IMUs	Ranavolo et al. (2017) [174]	20 M	Lifting task	All body	Biomechanical risk assessment using kinematic parameters	Kinematic indices (Lifting Anergy Consumption) were proved to be significantly growing with the LI, discriminating all the risk condition pairs and well correlating with forces that determine injuries at the L5-S1 joint	Joint angles, Center of Mass, Mechanical Energy
IMUs and sEMG	Peppoloni et al. (2016) [3]	8 M, 3 F	Real-life operations of super-market cashiers	Upper limbs	The system exploits IMU to reconstruct the upper limb posture, modeled as a 7- degrees of freedom kinematic chain. sEMG sensors are used to assess forearm flexor muscles strain	The system was capable of autonomously segmenting the cycles and giving a score for each cycle	Joint angles, sEMG
IMUs and sEMG	Brandt et al. (2018) [173]	26 M	Lifting task	Trunk	To classify lifting activities into low and high risk categories based on sEMG and trunk inclination measurements	Lifting tasks were characterized by a feature vector composed of either the 90th, 95th or 99th percentile of sEMG activity level and trunk inclinations during the task applying a linear discriminant analysis and a threshold scheme to classify the lifting tasks with an accuracy of 65.1–65.5%	Trunk inclination, sEMG
Dynamometer and sEMG	Ranavolo et al. (2017) [58]	7 M, 8 F	Manual handling of low loads at high frequency	Upper limbs	To assess the muscle fatigue of the flexor muscles before and after four levels of simulated manual handling of low loads at high frequency; to analyze the calculated fatigue indices to understand whether they correctly classify the risk; to correlate calculated fatigue indices to the muscle behaviors during the execution of the dynamic work task	Fatigue index calculated from the brachioradialis was sensitive to the interaction among risk classes, session and gender	Myoelectric manifestation of muscle fatigue
Hand-held dynamometer	Cadogan et al. (2011) [194]	23 M, 17 F	Active and passive shoulder motion	Shoulder	To establish the reliability of measures of shoulder range of motion (ROM) and muscle force	Active ROM (flexion) demonstrated high levels of both intra- and interexaminer reliability. Passive ROMs and isometric force peaks shown acceptable levels of intraexaminer reliability	Range of motion and muscle force
Grip dynamometer and sEMG	Blackwell et al. (1999) [205]	18 M	Isometric and submaximal gripping contractions	Flexor digitorum superficialis muscle	To investigate the effect of grip span on isometric grip force and fatigue of the flexor digitorum superficialis muscle during sustained voluntary contractions	Fatigue of flexor digitorum superficialis did not change as a function of grip size. Middle grip sizes allowed for greater absolute forces than the small or large size. When contractions are at 60–65% MVC and the muscle is allowed to fatigue, grip size may be less infuential than when maximal absolute force is required	Myoelectric manifestation of muscle fatigue
Dynamometer and sEMG	Roman-Liu et al. (2004) [182]	10 M	Exerting maximal force and tests under specific load conditions	Arm and hand muscles	To discriminate fatigue of upper limb muscles depending on the external load, through the development and analysis of a muscle fatigue index	External loads induced modifications in the fatigue of the biceps brachii caput breve, extensor carpi radialis brevis, and flexor carpi ulnaris muscles	Myoelectric manifestation of muscle fatigue
sEMG and electrogoniometers	Granata and Marras (2000) [57]	10 M	Lifting task	Trunk extensors and flexors muscles	To evaluate whether increased biomechanical stability associated with antagonistic co-contraction was capable of stabilizing the related increase in spinal load	Coactivation was associated with a 12% to 18% increase in spinal compression and a 34% to 64% increase in stability. Spinal load and stability increased with trunk flexion	Muscle coactivation, spinal load and stability
sEMG	Sundelin (1993) [166]	12 F	Repetitive arm work continuously without pauses and with pauses	Trapezius and infraspinatus muscles	Fatigue assessment in shoulder and neck muscles during continuous work and during work with organized pause activities	Muscle fatigue with a decrease in the mean power frequency and an increase in root mean square amplitudes was found both during continuous work and work with pause activities. The muscle fatigue was less pronounced when pause activities were introduced into the work. Fatigue patterns were lower during the second hour, indicating adaptation to the work task and work pace. The ratings of perceived exertion and discomfort were similar during work with and without pauses and were higher during the second hour of work	Myoelectric manifestation of muscle fatigue
sEMG	Sundelin and Hagberg (1992) [165]	6 F	Repetitive arm work for 1 h	Trapezius and infraspinatus muscles	Fatigue assessment in shoulder and neck muscles during work paced by the methods–time measurement system	Muscle fatigue with a decrease in the mean power frequency and an increase in root mean square amplitudes was found	Myoelectric manifestation of muscle fatigue
sEMG	Hansson et al. (1992) [167]	33 F	Static endurance test	Trapezius and deltoid muscles	Muscular fatigue assessment during a standardized isometric endurance test in women with a static workload, with and without neck/shoulder disorders	The endurance time for a group of women in industrial work with repetitive short cycled work tasks who were diagnosed with neck/shoulder disorders was significantly shorter than for a group with the same work, but without neck/shoulder disorders and shorter than for a control group. There were no significant differences in muscle fatigue between the three groups considered	Myoelectric manifestation of muscle fatigue
sEMG	Mathiassen and Winkel (1996) [164]	8 F	Assembly task with different combinations of work pace (120 or 100 according to the methods-time measurement system, MTM), break allowance and duration of the working day	Trapezius muscle	Fatigue assessment in shoulder and neck muscles in different industrial assembly task	During 6 h of work at 120 MTM the EMG amplitude from the upper trapezius muscle increased by about 11%, the EMG zero crossing rate decreased by about 2.5%, and perceived fatigue increased. When work pace was reduced to 100 MTM, the upper trapezius EMG amplitude decreased by 20% and became less variable, perceived fatigue decreased and shoulder tenderness was reduced by about 5%. Added breaks, whether active or passive, had no apparent effects on upper trapezius load during work or on physiological responses	Myoelectric manifestation of muscle fatigue
sEMG	Cabeças (2007) [204]	1 M, 19 F	Cleaning activities	Wrist flexor and extensor muscles	A modified application of the Strain Index method, in evaluation of effort-related variables in cleaning activities	EMG data were found to be a useful alternative to observational methods. The most critical cleaning activities and that with comparatively lower risk to distal upper extremity disorders were individuated	sEMG data (time, intensity, frequency of efforts)
sEMG	Bosch et al. (2009) [183]	5 M, 5 M	Assembly task (construction and break down a tower)	Trapezius muscle	To determine whether muscle fatigue develop in the upper trapezius muscle in two assembly tasks involving contractions of different low-intensity levels and whether these indications of fatigue are homogeneously distributed across different muscle parts	Recordings during task and test showed a significant decrease in the mean power frequency, at both intensity levels while the amplitude remained constant. Significantly different temporal patterns were found for the mean power frequency decrease. No differences in manifestations of muscle fatigue development were found between different parts of the muscle	Myoelectric manifestation of muscle fatigue
sEMG	de Looze et al. (2009) [162]	-	Repetitive low-level force activities	Shoulder muscles	Review of studies on objectively measurable fatigue related changes in time in low-level force activities	Electromyographic manifestations of fatigue in the trapezius muscle appear in low-force activities like light manual work and assembly when the intensity level is about 15–20% MVC. The amplitude increases ranged from 3% to 27%, while the mean power frequency decreases range from 0.9% to 11%. Furthermore, local muscle fatigue seems to occur in some light manual activities and could be considered a risk indicator	Myoelectric manifestation of muscle fatigue
sEMG	Jones and Kumar (2010) [87]	89	Sawmill work (Board-edger operator; Lumber grader; Saw filer; Trim-saw operator)	Wrist flexor and extensor muscles	To examine the agreement between 5 ergonomic risk assessment methods calculated on the basis of quantitative exposure measures and to examine the ability of the methods to correctly classify risk job	RULA and SI were best (correct classification rates of 99 and 97% respectively). The quantitative ACGIH-TLV for monotask hand work and Borg scale were worst (misclassification rates of 86 and 28% respectively)	sEMG and electrogoniometric data
sEMG	Nimbarte et al. (2010) [201]	10 M, 5 F	Lifting tasks carried out at shoulder height in extended, neutral, and flexed neck postures	Neck muscle (sternocleidomastoid and the upper trapezius)	To evaluate physical risk factors (force and posture) associated with neck disorders among construction workers	Increase in the weight lifted increased the activation of the neck muscles. The sternocleidomastoid muscle was most active at the extended neck posture, while the upper trapezius muscle was most active at the flexed neck posture	sEMG data
sEMG	Jia et al. (2011) [202]	19 M, 5 F	Carrying, erecting, lifting and moving tasks	Trunk muscles	Using a model, to predict trunk muscle forces and low back loads during a wide range of panel erection tasks	Reasonable levels of correspondence were found between measured and predicted lumbosacral moments, though predictive ability varied between tasks and rotation planes	Trunk muscle forces and low back loads
sEMG	Draicchio et al. (2012) [188]	10 F	Work activities of supermarket cashiers	Shoulder and trunk muscles	To provide a biomechanical evaluation of cashiers working at a checkout counter before and after a redesign, on the basis of changes induced in time, kinematic and electromyographic variables	The ergonomics intervention (disk wheel) represented a valid aid for reducing biomechanical overload in cashiers and the standing position resulted biomechanically more advantageous. The range of motion values of upper limb and trunk were lowest after the intervention and in the standing position	Range of motion and sEMG data
sEMG	Perez-Duarte et al. (2014) [203]	10 M, 4 F	Conventional laparoscopic and laparoendoscopic single-site surgery	Upper body	To determine inherent risk levels for wrist disorders assessing the degree of arm and back muscle activity as well as spatial configuration of hand and wrist	Muscular activity for trapezius and forearm extensor muscles was significantly lower in conventional laparoscopy compared with single-site approach. A better wrist position was found during laparoendoscopic single-site surgery compared with traditional laparoscopy	sEMG data
sEMG	Ranavolo et al. (2015) [156]	10 M	Lifting task	Trunk extensors and flexors muscles	A method developing for the monitoring of the co-activation of more than two muscles during lifting task	Heavier lifting conditions resulted in higher co-activation values	Muscle coactivation
sEMG	Silvetti et al. (2015) [186]	5 M	Manual handling on a supermarket greengrocery Shelf	Sholuder and trunk muscles	To investigate the effect of different shelf levels and load weights on the workers’ biomechanical load	Shelf level had a significant effect on most of the parameters examined. Weight did not affect the biomechanical load	Ankle joint range of motion and sEMG data
sEMG	Shair et al. (2017) [179]	-	Manual lifting	Arm and trunk muscles	To review the impact of EMG processing in fatigue assessment during manual lifting and to determine the best possible techniques for lifting applications	Bilinear Time-Frequency Distribution (TFD) could perform better than the linear TFD such as Short-Time Fourier Transform (STFT), spectrogram, and Wavelet Transform (WT). Bilinear TFD suffered the cross term effects, that could be removed	sEMG data
sEMG	Le et al. (2017) [65]	-	Isometric loading, Lifting, Isometric quasi-static exertions, motions at different speeds	All body	Understanding of the factors that may influence coactivation and define the necessary variables for a coactivation index that can be used for a variety of tasks	The index appeared to be sensitive to conditions where higher coactivation would be expected. These conditions of higher coactivation included tasks involving higher degrees of control. Precision placement tasks required about 20% more coactivation than tasks not requiring precision, lifting at chest height required approximately twice the coactivation as mid-thigh height, and pushing fast speeds with turning also required at least twice the level of coactivity as slow or preferred speeds	Muscle coactivation
sEMG	Le et al. (2017) [66]	7 M, 10 F	Lifting/lowerin, pushing, and Valsalva maneuvers	Trunk extensors and flexors muscles	To describe the development of an index to assess coactivity for the lumbar spine and test its ability to differentiate between various complex dynamic tasks	Coactivity for dynamic tasks necessitates the understanding of local maxima and minima, different phases of loading, its effect on peak spinal loads, and cumulative responses. It was postulated that a mind–body interaction exists which warrants a continuously defined agonist/antagonist coactivation index sensitive enough to detect those differences	Muscle coactivation
sEMG	Le et al. (2018) [175]	5 M, 7 F	Different combinations of head posture and speed of movement	Neck muscles	Develop a coactivation index for the neck and test its effectiveness with complex dynamic head motions	Complex motions involving twisting and higher speed had higher magnitudes of coactivation than uniplanar motions in the sagittal or lateral plane, which was expected. The coupled motion of flexion and twisting showed four to five times higher coactivation than uniplanar movements	Muscle coactivation
sEMG	Ranavolo et al. (2018) [177]	16 M	Lifting task	Trunk extensors and flexors muscles	sEMG activities of the trunk muscles and forces at the L5-S1 joint to identify sEMG-based indices related to the risk level and to the forces at the L5-S1 joint	sEMG indices were proved to be significantly growing with the LI, discriminating all the risk condition pairs and well correlating with compression and shear forces that determine injuries at the L5-S1 joint	sEMG data, muscle coactivation and forces at the L5-S1 joint
sEMG	Le et al. (2018) [176]	7 M, 10 F	Lifting/lowering, pushing and Valsalva manoeuvres	Trunk muscles	To provide an approach to assess multi-muscle coactivation comparing this index to a coactivation index defined by a biologically assisted lumbar spine model to differentiate between tasks	The EMG-based index was comparable to the index defined by a biologically assisted model. The EMG-based index provided a universal, usable method to assess the neuromuscular effort associated with coactivation for complex dynamic tasks	Muscle coactivation
sEMG	Varrecchia et al. (2018) [178]	10 M	Lifting task	Trunk extensors and flexors muscles	Biomechanical risk assessment using sEMG and neural network	Approaches based on machine-learning techniques and sEMG feature led to an improved biomechanical risk classification	sEMG data in time and frequency domain