Keywords

1 Introduction

Despite the increasing trend toward automation and industry 4.0 in production systems [1, 2], human operators will remain a central player and factor in industrial factories [1, 3, 4]. It is expected that future-proof jobs in production will be characterized by human–machine interaction [1, 5, 6], hybrid systems consisting of human and robotic operators [2], and a paradigm shift from task-centric to human-centric workplaces [3, 7]. Due to the remaining share of manual work, workers will continue to be exposed to the risk of suffering from musculoskeletal disorders (MSDs), which are the most common reason for sick leave in industrial occupations [8,9,10]. Concerning assembly tasks, working in particularly stressful and unergonomic postures as well as repetitive work processes, such as overhead work, are a decisive risk factor for causing upper extremity MSDs [8,9,10,11] and stress the increasing importance of their prevention and an ergonomic work design [5, 7,8,9,10]. Support systems such as exoskeletons are one possible remedy, with the potential to relieve users during the execution of their work [3, 4, 6]. However, exoskeletons are not widely used in the industry yet, as evidence of the relief effects of exoskeletons, especially in the long term, is scarce [4, 12]. In terms of exoskeletons supporting overhead work, the literature predominantly describes studies with passive exoskeletons since no active shoulder-supporting exoskeletons are currently available on the market. The article starts at this point and presents a laboratory test setup of an overhead assembly task, which allows a combined subjective and objective assessment for both an exemplary passive and active exoskeleton. Thus, it operationalizes a station from a test course for industrial exoskeletons [13] and enables a pre-study on the exoskeleton’s contributions to user support and future ergonomic workplaces.

2 State of the Art

For the multicriteria evaluation of exoskeleton’s supportive effects, a multitude of criteria and methods are applied, which are suitable for evaluating different support scenarios to varying degrees but are mainly performed in laboratory environments up to now [4, 14]. Depending on the desired focus, subjective (e.g., Borg scale, observations) and objective (e.g., electromyography, motion capture) evaluation methods are capable of delivering results, of which the examination of the physical relief by means of electromyography (EMG) is the most frequently used method [14]. However, a comprehensive evaluation includes complementary subjective and objective measurement methods [4, 11, 12, 14,15,16,17].

Different foci of investigation are set in laboratory settings, relating to the study of either singular tasks at workstations or more complex processes at integrated workplaces or in test courses [13]. A considerable number of laboratory studies have already been conducted to measure muscle activity during overhead assembly tasks. Thus, the unloading effect of exoskeletons during drilling in different directions, force application points, and body postures has already been investigated [18, 19]. In other articles, subjective criteria are examined in addition to muscle activity. Objective measurements during overhead tasks such as drilling, riveting, grinding, or lifting heavy objects are supplemented by surveys on, e.g., perceived discomfort and sense of stress [11, 12, 15, 16]. Overhead assembly tasks are also studied in industrial environments using EMG and questionnaire studies or Borg scales [12, 17].

However, almost exclusively passive shoulder-supporting exoskeletons have been investigated in previous studies. Therefore, a novel aspect of this work is the comparative evaluation of the suitability of both an active and a passive system concerning the support effect for an exemplary application scenario.

3 Materials and Methods

For evaluating the support effect of exoskeletons, a characteristic overhead assembly task was considered, which the subjects performed with and without exoskeleton support. Its test setup followed a proposed approach of laboratory-based modeling of industry-related tasks [13].

3.1 Study Participants

The study population included four volunteered right-handed males, all of whom were in a physically healthy condition and did not report current shoulder pain. The subjects had an age between 21 and 24 years (mean: 22.5 years), a height between 174 and 190 cm (mean: 180 cm), and a weight between 65 and 83 kg (mean: 75.3 kg).

3.2 Test Setup

The task consisted of setting and fastening two bolts side by side in a wooden beam (mounted at a reference height of 2.1 m), using an electric screwdriver of mass 2.55 kg. The start and end pose of the task were equal, where the screwdriver was held in an angled arm position without the tool in reach. During the execution, the two bolts were first set and then fixed in the wooden beam by a vertical upward movement of the arm. The subjects were not given any specific instructions regarding the speed at which to perform the task. However, the execution of the screwing process should be similar in all runs. For high comparability between runs and subjects, the task was performed in a standardized manner. Accordingly, the investigation focused on the screwing as core and excluded, e.g., the gripping of the screwdriver. Besides, the mounting position of the beam was individually adjusted to the subject’s height allowing subjects to consistently perform the task in an upright posture and guide the screwdriver with the dominant hand while the non-dominant hand set the bolts. In addition, the lower and upper arms were at right angles to each other during the screwing. Each subject performed the screwing in triplicate: (A) without exoskeleton support as well as with support by a (B) passive and (C) active exoskeleton. Figure 1 illustrates an excerpt from the task showing the exact pose in the baseline (left) and supported (right) scenario.

Fig. 1
figure 1

Assembly task for baseline (left) and supported scenario with Lucy 2.0 (right)

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3.3 Used Exoskeletons

A passive (Skelex 360) and an active (Lucy 2.0) exoskeleton were used as examples to evaluate the support effect of exoskeletons for overhead assembly tasks. The passive exoskeleton Skelex 360 provides a supportive force when lifting the arms, thus counteracting the arm’s force of gravity [20]. Two carbon-fiber leaf springs generate support and compensate for a weight of up to 3.5 kg per arm [20]. The maximum supporting torque equals six Nm [12]. Equal to Skelex 360, the active exoskeleton Lucy 2.0 mainly supports the users performing tasks at or above head level [21]. The main difference lies in the generation of the supporting force. Lucy 2.0 uses rigid shoulder kinematics with inserted pneumatic actuators for creating the support effect [21]. By this actuation principle, the level of support can continuously be controlled [21] to generate a maximum torque of approximately 8.5 Nm at an arm bending angle of 85 degrees [15]. Before performing the task with exoskeletons, the subjects got familiar with the systems.

3.4 Applied Evaluation Methods

For comprehensively evaluating the support effects of both exoskeletons, the assessment combines a questionnaire survey of the subjects and an electrophysiological measurement of muscular activity. In the closed questionnaire study, (1) the perceived exertion and (2) the perceived support effect provided by the exoskeletons were asked for after performing the task. In contrast, EMG tracks the muscular activities of the medial deltoid (shoulder) and the erector spinae (back extensor) during the execution of the task. EMG uses surface electrodes and measures electrical signals in the microvolt range emitted by muscle cells [22]. The EMG sensors were placed on the muscles according to the SENIAM guidelines and in the fiber direction. Wireless surface EMG (Myon, Aktos, 960 Hz) was used during the studies.

3.5 Data Acquisition and Processing

Before performing the task, the maximum voluntary contraction (MVC) was measured for each subject to determine his peak muscular activity for the later analysis [23]. These MVC measurements formed the basis for the subsequent normalization of the data. Afterward, the muscle signals were recorded during the execution of the task with a frequency of 1000 Hz. However, these raw signals are not sufficient for evaluating the effectiveness of exoskeletons. The obtainment of meaningful results requires a data transformation of the EMG amplitude to a relative scale (% MVC) [23, 24]. Therefore, a four-step procedure is necessary: (a) rectification and filtering of the raw signal (for the generation of positive and filtered signals), (b) MVC-normalization (for the elimination of the influence of technical, anatomical, and physiological influences as well as for better illustration and comparison of stress levels), (c) activity separation (for cutting the relevant activity sequences from the entire signal), and (d) time normalization (for tailoring and relativization of task durations between subjects) [23, 24]. Statistical parametric mapping (SPM) [25] helped analyze and interpret the EMG data. Within this frame, statistical methods tested hypotheses for region-specific effects [25] between the baseline scenario and the scenarios with exoskeleton support. A nonparametric, unpaired two-sample t-test checked the data for mean differences at a significance level of five percent. By comparing the scenarios with and without an exoskeleton, the effect on the muscular activities was investigated at each point in time. As a result, movement sequences were determined the signals significantly differed and, thus, an effect of the exoskeleton existed. Each of the four subjects screwing two bolts per scenario doubled the total data pool to eight measurement sets.

4 Results

This section describes the results of the studies conducted. First, the results of the questionnaire study are presented, followed by those of the EMG study.

4.1 Results from Questionnaire Study

The results from the questionnaire study on the (1) perceived exertion and (2) perceived support effect provided by the exoskeletons are illustrated using the Borg RPE scale (6–no exertion to 20–maximum exertion) [26] and Likert scale (1–low to 5–high), respectively. The data are presented as boxplots to visualize the median and standard deviation. Additionally, a dot within the boxplot indicates the mean value.

The first question evaluated the rate of perceived exertion (RPE). For this purpose, the subjects assessed their RPE for each of the three executed runs of the task. The left-hand chart in Fig. 2 shows the results of this survey. The three boxplots display the evaluation for the investigated scenarios (A) without exoskeleton support (left plot) as well as with support by (B) Skelex 360 (middle plot) and (C) Lucy 2.0 (right plot). For the baseline scenario, i.e., executing the task without exoskeleton support, a mean RPE value of 10.75 was determined. According to the Borg scale, this corresponds to a light perceived exertion [26]. Performing the task with the support of an exoskeleton resulted in a mean RPE of 8 (Skelex 360) and 7.5 (Lucy 2.0), respectively. These ratings each correspond to a level of effort perceived as extremely light [26]. However, the width of the boxplots illustrates a broader distribution in subjects’ assessments of exoskeletal support compared to the baseline scenario. Accordingly, there is a higher divergence in evaluating (B) and (C). Nevertheless, the RPE mean value notably differs for the supported scenarios compared to the non-supported scenario.

Fig. 2
figure 2

Results from study on perceived exertion (left) and perceived support effect (right)

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The second question evaluated whether the subjects felt a supportive effect of using Skelex 360 and Lucy 2.0. The right-hand chart in Fig. 2 shows the results of this survey. The perceived supportiveness of Skelex 360 (with a mean of 4.5) and Lucy 2.0 (4.75) was rated as high for both exoskeletons. Accordingly, the subjects’ ratings indicated a perceived support effect of both Skelex 360 and Lucy 2.0.

For both (1) the perceived exertion and (2) the perceived support effect, the evaluations of the questionnaire study indicate a support effect by Skelex 360 and Lucy 2.0. However, since the results so far are only based on the subjective assessment, the results of an additional objective measurement of muscle relief are described below.

4.2 Results from EMG Study

This section describes the analysis and evaluation of the EMG investigation. Figure 3 shows the results of evaluating the passive exoskeleton Skelex 360 compared to the baseline scenario. As the lower graph of Fig. 3 shows, the t-value exceeds the reference value between 45% to 78% and 86% to 94% time relating to the significance level (p-value = 0.014). In the subject context, this means the subjects were supported during large time fractions in the second half of the task execution, in which they screwed overhead with the dominant hand. Moreover, the peak in significance around 90% of the temporal performance is striking, where the bolt was sunk into the wooden beam with a slightly increased force applied. Accordingly, the analysis detects significant support for the deltoid muscle by Skelex 360 in the named ranges. On this basis, the curves of the relative muscular activity (in % MVC), shown in the upper graph of the figure, can now be interpreted. For the significant time portion of the support, the relative muscular activity while using Skelex 360 equaled 15% MVC over most of the task execution. Its use resulted in a muscular relief for the medial deltoid of 10.8%-points concerning the MVC measurement. Appropriately, using Skelex 360 revealed a maximum unloading effect of 40.6% during the task fraction of overhead screwing. For the other task fractions, there was no significance according to SPM. This fact implies the curves do not lead to any meaningful interpretation. The same result applies to the support of the erector spinae muscle, where no significant support resulted for the entire course of the task.

Fig. 3
figure 3

Analysis of the support effect for Skelex 360 in terms of significance (lower graph) and reduction of activity for medial deltoid muscle (upper graph)

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Similarly, Fig. 4 shows the analysis results with the active exoskeleton Lucy 2.0 compared to the baseline scenario. As the lower graph in the figure shows, the t-value exceeds the significance threshold over almost the entire task course (p-value = 0.014). Consequently, significant support by Lucy 2.0 was detected for the deltoid muscle over nearly the complete task execution (setting the bolts and screwing overhead), except for the last five percent of the time (lowering the dominant hand holding the screwdriver). The three sections of the movement sequence, beginning of elevating the arm to set the bolts, (first) applying the torque during the screwing, and countersinking the bolt in the beam, reached the highest significance. For the significant time portion, the relative muscular activity using Lucy 2.0 was 10% MVC in the first part of the task (setting bolts) and increased to 15% MVC in the second part (screwing overhead). Accordingly, the second part of the task required higher muscular activity. Overall, the use of Lucy 2.0 resulted in a relief of the medial deltoid muscle of 12.2%-points regarding the MVC measurement. The upper graph visualizes a maximum unloading effect of 49.6% for Lucy 2.0. Equal to the run with Skelex 360, there is no significant unload of the erector spinae muscle.

Fig. 4
figure 4

Analysis of the support effect for Lucy 2.0 in terms of significance (lower graph) and reduction of activity for medial deltoid muscle (upper graph)

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Consequently, the statistical analysis of this task supports the results of the subjective questioning and shows significant support potentials regarding the reduction of muscle activity by using both exoskeletons.

5 Discussion

In this section, the results obtained in the study are abstracted regarding limitations in the study design and results, lessons learned, and implications on future workplaces.

5.1 Limitations of Studies and Results

First of all, it is crucial to stress that the results base on the specific test design described in Chap. 3.2 and are only valid in this respect. Accordingly, the obtained results depend on the task, its execution by the respective subjects, and the exoskeletons used. Limitations in the test design include the standardization of the processing and the time required for test persons to become accustomed to using exoskeletons. Both factors influence the execution of the tasks, and, thus the reproducibility of the results since individual movement behavior and longer familiarization with the exoskeletons might produce different results. In combination with these two aspects, the study was conducted with four exclusively young and male subjects in good physical condition, not being the only reason why a larger sample is a relevant factor for improved evidence and higher informative value of the results. Regarding evaluating the measurement data, the processing and cutting of the measurement signals also play a role [23, 24]. All these aspects influence the validity and especially the reliability of the results.

5.2 Lessons Learned from Studies

The article indicates it is not feasible to make a blanket statement about an (unlimited) support of an exoskeleton. The results must always be related to individual sections of motions and can only be evaluated against the task. Especially in the example of Skelex 360, the analysis of the results shows the relevance of dividing the complete task into single fractions. The same effect also applies to analyzing the muscular unloading effect caused by exoskeletons. Even if the curve progressions show a different level in terms of relative strain, no meaningful interpretation is valid unless significance is proven. Besides, relieving effects of exoskeletons can only be compared against each other if the support characteristics and torques induced by the exoskeletons are identical over the course of the angle. As a result, the study stresses the importance of equally considering subjective and objective criteria in the evaluation, as they can provide complementary results. Notwithstanding this, the results of the objective EMG investigation provide better empirical evidence than those of the questionnaire study.

5.3 Implications on Future Workplaces

The results reinforce using exoskeletons as a considerable approach while designing sustainable and ergonomic industrial workplaces. Particularly against the background of trends such as human-machine interaction [1, 5, 6] and user-centric workplace design [3, 7], exoskeletons can significantly contribute to supporting employees while maintaining their flexibility in manual work processes simultaneously [1]. Besides, support systems such as exoskeletons offer the opportunity to preserve human skills and abilities [27] and provide physical relief at the same time. Thus, using exoskeletons can constitute an attractive and human-oriented initiative to maintain the employee’s health.

6 Conclusion and Outlook

This article describes the modeling of an exemplary overhead assembly task in a laboratory environment and its execution in different test scenarios with and without exoskeleton support. The support effects for Skelex 360 and Lucy 2.0 were evaluated. Plans include expanding the studies to a larger collective of subjects, tasks, and exoskeletons. Additionally, it seems reasonable not only to investigate the effect of exoskeletons in terms of physical but also cognitive support. However, within the framework of this study, the article provides evidence that passive and active exoskeletons can lead to (objectively verifiable) muscular and (subjectively) perceived physical relief in separate movement sequences and tasks and, thus, can become a considerable element of ergonomic and human-centric industrial workplaces with future orientation.