Human Performance Measures

Currently, there exist over 500 distinct human performance measures for evaluations of human functionalities and capacities associated with different domains, such as posture, strength, energy, fatigue, and so on [54]. In this section, posture evaluation methods and energy evaluation methods which will be used in the later investigation are reviewed separately.

a. Posture Evaluation Methods

Posture analysis is one of the most important aspects in human performance evaluations. Govindaraju mentioned that when the human body was exposed to discomfort, its natural reactions would slow down in order to minimise the accumulation of discomfort, and avoid or reduce the manifestation of pain [55]. Psychologically, when the operator starts to feel fatigue, his motivation to keep performing at optimal levels is significantly reduced. As a result of the reduced performance, human errors could increase, which in turn increases the risk of accidents and loss of quality. The analysis of posture is therefore necessary.

Posturegram is one of the first methods developed to numerically quantify human postures [56]. From repeated observation of operators, the basic body posture in a three-dimensional coordinate system, the levels at which joints and limbs are located, and the direction and amount of movements within the

three-dimensional coordinate system are defined and recorded on a Posturegram card. By creating a standard base posture, it was the first method describing the posture deviation from a start position.

The Ovako Working Posture Analysing System (OWAS) is another practical method for unsuitable working postures identification and evaluation [19, 57]. In OWAS, a coding system as shown in Table 2.3 is established to evaluate each posture corresponding to the discomfort or risk it caused. Each posture is described with a 4 digit code. After that, action categories are given a rank from 1-4 with 4 being the highest risk to the musculoskeletal discomfort. Subjective evaluations of each posture’s code are categorised into one of the 4 action categories. Applied initially for a company in Finland steel industry, it has now been integrated into a substantial of ergonomics software tools for manual task investigation.

Table 2.3: OWAS coding system [19]

Body region Posture or weight Risk rank

Back

Straight 1

Bent 2

Twisted 3

Bent and twisted 4

Upper Extremity

Both below shoulder height 1 One above shoulder height 2 Both above shoulder height 3

Lower Extremity

Sitting 1

Both legs straight (Standing) 2 one leg straight (Standing) 3 Both legs bent (full squat) 4

one leg bent 5

Kneeling 6

Walking 7

Force or load effort

≤10 kg 1

≤20 kg 2

>20 kg 3

Similar to OWAS, a rapid upper limb assessment (RULA) method is developed which uses the concept of numbers to describe postures with an associated coding system [58]. It is of particular assistance in fulfilling the assessment requirements of the UK guidelines to prevent work-related upper limb disorders and recently has become a programming ergonomics analysis tool as

well.

Rapid Entire Body Assessment (REBA) method is developed as a practitioner field tool, which is specifically sensitive to the type of unpredictable working postures found in health care and other service industries [59]. By coding and ranking over 600 postural examples collecting from hospital industries, a final REBA score (1-15) is established with accompanying risk and action levels.

Besides these methods, some checklist tools are developed to assess postural risks rapidly, e.g. PLIBEL and Postural Checklist [60, 61]. PLIBEL (method for the identification of musculoskeletal stress factors which may have injurious effect), designed and tested in Sweden, is developed to determine tasks’ contribution to WMSDs and now has been used in a variation of environments from manufacturing industries to service industries. It includes a list of seventeen total “yes/no” questions which relate to individual body regions to identify whether they cause WMSDs. Table 2.4 gives some questions relating to the low extremity in the PLIBEL. Postural Checklist was originally developed for management of automotive manufacturing. Postures in checklist are grouped according to different body segments. Qualitative stress rating responses for each of the body segments can be given as zero, check or star. A total risk score for a task was quantified by adding the total number of checks with the total number of stars, as shown in Table 2.5.

Table 2.4: PLIBEL questions relating to the low extremity [60] Question number Related question for feet, knee and hip body regions

1 Is the walking surface uneven, sloping or slippery?

2 Is the space too limited for work movements or work materials?

3 Are tools and equipment unsuitably designed for the worker or the task?

6 (If the work is performed whilst standing): Is there no possibility to sit and rest? 7 Is fatiguing foot-pedal work performed? 8 Is fatiguing leg work performed?

In reviewing the posture evaluation tools above, some limitations of the methods based on the coding system or checklist should be noted. First, a

Table 2.5: Stress rating response and their explanations in postural checklist

[61]

Stress rating response Explanation

Zero Using the posture for the indicated duration presented the insignificant risk of injury or illness. Check Moderate exposure to postural stress was presented

indicating a potential risk of injury to some workers Star Substantial exposure to postural stress was presented

indicating significant risk of injury

flexible range of joint movement was typically divided into several sections and each section was simply assigned by a score. Furthermore, the posture score had a consistent increment of ‘1’ according to the joint section or the number of checks. The score ‘1’ is given to the working posture where the risk factors present are minimal and the higher scores are allocated to more extreme postures indicating an increasing presence of risk factors. For example, a calculation of lower arms score using the RULA method is shown in Figure 2.4 [59]. It is observed that only two scores (1 and 2) are used to describe the posture discomfort resulting from the lower arm movements. Though the method is easy and rapid used by the analyst, limited scores can not represent differences existing among considerable joint movements sufficiently and accurately. Also, due to the physical properties of joints, their influence on the discomfort score can not be identical. Therefore, a more detailed analysis should be conducted in order to better investigate the exclusive contribution of joint posture to the discomfort and risk.

Figure 2.4: The lower arm posture score calculation [59].

b. Energy Evaluation Methods

Energy expenditure rates are examined typically by ergonomists to assess physiological demand on workers. In theory, if workers are required to exert less than 50% of their energy expenditure capacity during a work day, then they

should not become physiological strained. Therefore, a predictive method for assessing metabolic rate of work has appealed to practitioner as an invaluable tool due to its major advantage on the job design.

Traditionally, many researchers had used the measurement of oxygen utilisation rate to predict the metabolic energy expenditure [62, 63]. However, due to interference of measuring equipments with the normal work methods, its results may be not valid.

A table look-up approach can provide a rough approximation of the metabolic energy expenditure of average operator in performing manual activities. A large number of tables, in which occupational task and grouped together according to their metabolic demands, can be found in the literature [64, 65].

Garg proposed a metabolic rates prediction approach for manual materials handling tasks [66]. The average rate of a handling task can be estimated by summing up the basic energy require to maintain a body posture and the net energy cost for lifting, carrying and walking.

Burford developed a systematic workload estimation (SWE) method for assessment of the metabolic cost of work performed in underground mines [67]. In SWE, the analyst conducts estimation of metabolic rate by coding tasks according to schema and then the codes are converted into their caloric values.

Metabolic energy expenditure rate has been often suggested in literature for determining the maximum task intensity which can be continuously performed without accumulating an excessive amount of physical fatigue. Hence energy expenditure prediction can also be related to fatigue prediction. At the biomechanical level, literature [68] explains fatigue as loss of energy. In the human gait motion, Anderson and Pandy suggested that minimising muscle fatigue at each instant is roughly the same as minimising metabolic energy expended per unit distance travelled over the duration of the gait cycle [69]. In fact, it is well known that energy expenditure and muscle fatigue have positive correlation [70]. Therefore, minimum metabolic energy expenditure indicates less muscle fatigue as well.