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Article

Simulated Intelligent-System Interruptions: Effects on Back-Support Exoskeleton Performance and Muscle Activation

by
Jeewon Choi
*,
Junik Park
and
Chaerim Park
Department of Industrial Management Engineering, Dong-A University, Busan 49315, Republic of Korea
*
Author to whom correspondence should be addressed.
Actuators 2025, 14(11), 555; https://doi.org/10.3390/act14110555
Submission received: 9 October 2025 / Revised: 10 November 2025 / Accepted: 11 November 2025 / Published: 13 November 2025
(This article belongs to the Special Issue Human-Centric Intelligent Actuation Systems: Innovations in Control, Sensing, and Multidisciplinary Applications)
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Abstract

This study examined how interruptions, which are increasingly prevalent in modern intelligent work systems, influence the effectiveness of a back-support exoskeleton (BSE) during repetitive low-load lifting. Thirteen healthy male participants (age: 22.7 ± 1.7 years) performed a repetitive lifting task with and without a back-support exoskeleton (BSE) while concurrently engaging in a digit subtraction task that simulated cognitive interruptions characteristic of intelligent systems, presented at three frequencies (none, intermittent, and frequent). Task performance (number of lifting repetitions and placement accuracy), muscle activation of the erector spinae and upper trapezius, and subjective workload were assessed. Results showed that BSE use reduced the number of lifting repetitions by approximately 8% but did not affect placement accuracy. Consistent with its intended function, the BSE decreased erector spinae activation and subjective workload; however, it was also associated with progressively greater trapezius activation. Notably, BSE did not provide additional benefits under these cognitively demanding conditions, highlighting its limited effectiveness when attentional resources are constrained. These findings highlight that the value of BSEs depends not only on biomechanical support but also on work environments that effectively manage the dual-task demands introduced by intelligent systems.

1. Introduction

Workers in modern industrial environments equipped with intelligent systems are frequently exposed to cognitive interruptions. This occurs because automated intelligent systems increasingly shift workers’ roles from direct task execution to continuous monitoring [1,2], introducing multitasking demands that increase the frequency of cognitive interruptions [3]. Thus, modern workers face not only traditional interruptions, such as questions from coworkers or unexpected noises (i.e., external interruptions), and internally generated thoughts unrelated to the task (i.e., internal interruptions) [4], but also notifications and alarms generated by intelligent systems that attempt to capture their attention or prompt immediate responses. The prevalence of such interruptions has increased further in recent years with the widespread use of smartphones, smartwatches, and other communication devices that generate frequent notifications [5]. A large body of literature indicates that such interruptions require a worker’s attentional resources and can affect performance outcomes depending on their frequency, complexity, and timing [6,7,8]. These relationships can be interpreted within the framework of dual-task interference theory, which posits that concurrent cognitive and motor demands compete for limited attentional resources or for information-processing neural pathways [9,10]. However, the findings are mixed; some studies report impaired cognitive performance under frequent interruptions [11], while others show increased task speed at the cost of elevated stress levels [12,13].
While cognitive interruptions are increasing within intelligent systems, physical tasks such as object lifting remain a fundamental role for workers in industrial settings such as logistics, manufacturing, and construction. Excessive load handling is a well-known major contributor to lower back pain due to increased spinal loading [14]; however, even repetitive lifting of relatively light objects can result in cumulative physical strain [15,16]. The National Institute for Occupational Safety and Health (NIOSH) lifting equation, which recommends 23 kg as the optimal load under ideal conditions, also shows that when the lifting frequency exceeds 10 repetitions per minute, the recommended weight limit drops to less than 55%, even if other factors (e.g., horizontal distance and vertical displacement) remain optimal [17]. In addition to load-related factors, the need for the precise placement or careful handling of fragile items can increase workers’ psychological stress and cognitive demands.
Back-support exoskeletons (BSEs) have been developed as wearable assistive devices for reducing spinal loading and alleviating lower back pain during lifting [18,19]. Numerous studies have shown that both commercially available and prototype BSEs can reduce the activation of erector spinae (ES) muscles and trunk torque during lifting tasks [20,21,22]. These effects are generally enhanced following an adaptation period, during which the user becomes accustomed to the device [23,24]. BSEs are typically classified as either active, using powered actuators or motors, or passive, using springs or elastic elements [25]. While active BSEs offer advantages in terms of output strength and parameter tunability, passive BSEs are often favored by general users because of their simplicity and ease of use [26]. Notably, evidence suggests that passive BSEs are particularly well suited for handling light loads [27].
However, recent studies indicate that the effectiveness of passive BSEs can vary depending not only on physical task characteristics [28,29] but also on environmental and cognitive factors that influence how users interact with the device [30]. Excessive cognitive load is known to impair work performance by reducing attention and increasing errors or movement inaccuracies [31]. Although evidence suggests that active exoskeletons may impose relatively higher cognitive load due to the need for continuous parameter adjustments [32], even passive types can introduce additional burden on the user because of the device’s own weight and the unidirectional nature of external actuation. Furthermore, in automated environments where workers frequently manage multiple responsibilities and experience cognitive interruptions [33,34], these additional mental demands may interfere with task performance and postural control, thereby further diminishing the intended benefits of BSE use.
Therefore, in this study, we investigated how the effectiveness of BSE use is modulated by the frequency of cognitive interruptions. Among the various commercially available BSEs, the Laevo FLEX (Laevo Exoskeletons, Rijswijk, The Netherlands) was selected for the experiment and worn by participants who had completed a familiarization session with the device. Digit subtraction tasks were employed as representative cognitive interruptions during low-load high-repetition lifting tasks. To quantitatively assess task performance, the number of lifting repetitions was measured, and an infrared distance-sensing system was implemented to calculate the final position of the lifted object. The physical load on the upper body during the task was estimated by measuring electromyographic (EMG) amplitude in the ES and upper trapezius (UT), which are known to respond to both physical and cognitive demands. The subjective workload was evaluated using the NASA Task Load Index (NASA-TLX) questionnaire. We hypothesized that a higher frequency of interruptions would increase muscle activation and reduce task performance in BSE conditions. Additionally, we expected that BSE usage would generally lead to lower muscle activation and subjective workload regardless of the interruption frequency.

2. Methods

2.1. Participants

This study was conducted in accordance with the American Psychological Association Code of Ethics and the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of Dong-A University, and informed consent was obtained from all participants. Thirteen healthy male individuals voluntarily participated in this study (age: 22.7 ± 1.7 yrs; height: 175.0 ± 3.3 cm; weight: 78.1 ± 12.2 kg). The inclusion criteria required participants to have (1) attended at least 6 h of workshops on ergonomic lifting postures, (2) no low back pain in the past year, (3) no limitation in physical functions, (4) a height between 170 and 180 cm, and (5) a body mass index (BMI) less than 30 kg/m2. Using G*Power (version 3.1.9.7; University of Düsseldorf, Düsseldorf, Germany), an a priori power analysis was conducted with the statistical power (1 − β) set at 0.80, with a level of significance (α) of 0.05, 4 measurements, and an effect size of 0.42 based on the pilot test data. The analysis indicated that a minimum sample size of N = 10 would be required.

2.2. Experimental Setup

The participants were instructed to stand 40 cm away from the experimental rack. If the specified distance caused any discomfort, adjustments within ±5 cm were made to accommodate individual preferences. In this experiment, a plastic box with hand holes on both sides (external dimensions: 320 × 400 × 110 mm) was utilized as the working load. To replicate the relatively lightweight loads typically encountered in industrial settings, the weight of the box was standardized to 7% of each participant’s body weight. We determined the lifting load based on the NIOSH lifting equation for the experimental setup (horizontal multiplier = 0.63; vertical = 0.82; distance = 0.86; frequency = 0.52; asymmetry = 1; coupling = 1; load constant = 23 kg) [17]. The resulting recommended weight limit was approximately 5.3 kg, corresponding to 7% of the participants’ mean body weight (78.1 kg). The load material placed within the box consisted of a mixture of pebbles and weights. Orange tape (width: 1 cm) was affixed to both the box and the upper shelf (Figure 1b). On the box, the tape was attached vertically to the center of its front, whereas on the upper shelf, it was affixed parallel to the edge and positioned 60 cm from the left end of the shelf.

2.3. Repetitive Lifting Task

The participants were required to perform a repetitive lifting task by transferring the experimental box from the lower rack (10 cm above the ground) to the upper shelf (117 cm, equivalent to 67% of the average participant height) for 100 s at a self-selected pace. In this study, we intentionally avoided prescribing a standardized pace, as external cues like a metronome could mask the auditory interruptions, while consciously maintaining a set pace without such cues could itself create internal interruptions [4]. During the task, the participants were instructed to align the tape on the box with the tape on the upper shelf as precisely as possible to ensure accuracy and consistency in placement of the box.

2.4. Distance Sensing System

A distance-sensing system was devised to quantitatively assess the accuracy of box placement within a predefined area on the upper shelf. The system includes two infrared proximity sensors (SHARP 2Y0A21) positioned parallel to each other at the left end of the upper shelf (Figure 1a). Additional components included an Arduino UNO R3 microcontroller, breadboard, universal serial bus connectors, and Arduino IDE. The proximity sensors detected targets within a range of 10–80 cm, which is sufficient for precisely detecting deviations from the intended placement. When no distance was detected, the random sensor output values were between 20 and 25 cm. The distance signals detected by the sensors were sampled at a rate of 10 samples/s by using an Arduino microcontroller. The sampled signals were transmitted in real time to an Excel spreadsheet using the Parallax Data Acquisition tool (PLX-DAQ).

2.5. BSE

The BSE tested in this study was the Laevo FLEX (Laevo Exoskeletons, Rijswijk, The Netherlands), which is commercially available to both consumers and researchers. It is designed to be worn in alignment with the user’s greater trochanter and to generate supportive sagittal plane extension torque around the hip joint. The gas spring actuator strength of the BSE was set to medium, which delivers an average extension assistance moment of 25 Nm (maximum moment: 41 Nm) at the hip joint [35]. The weight of the BSE was 4.2 kg.

2.6. Interrupting Task

The interruption of the tasks began 20 s after the lifting task began. These tasks required participants to solve one- or two-digit subtraction problems in 3 s, which were displayed on a 24-inch monitor positioned 150 cm above the floor to the left of the experimental rack (Figure 2a). To ensure participants’ attention, a pure tone at a frequency of 1000 Hz and a sound pressure level of 70 dB was emitted for 0.2 s as an auditory cue (Figure 2b).
The participants were instructed to provide verbal responses to the displayed problems as quickly and accurately as possible, and the responses were recorded on a data sheet. Interruptions were implemented during the middle 60 s of the 100 s lifting task (interruption phase), excluding the initial and final 20 s (pre-interruption and post-interruption phase, respectively). Interruption frequency was categorized as either no interruption (control), intermittent (three times at 20 s intervals), or frequent (seven times at 10 s intervals).

2.7. Measurements

Surface EMG data from the ES and UT muscles were obtained using a Delsys Trigno wireless EMG system (Trigno™ EMG System, Delsys, Inc., Natick, MA, USA). The surfaces of these muscles were palpated and cleaned with alcohol, and two wireless sensors with active and ground electrodes were placed in line with the direction of the fibers of each muscle, following the guidelines of a previous study [36]. For the ES, the electrodes were positioned at the L1 level, whereas for the UT, they were placed at the midpoint of the line connecting the C7 spinous process to the acromion. The EMG signals were sampled at 2 kHz, bandpass filtered (10–500 Hz for the UT; 10–400 Hz for the ES), full-wave rectified using Labchart 8 software (ADInstruments, Castle Hill, Australia), and stored on a computer.
A series of maximum voluntary contractions (MVCs) for each muscle was conducted to obtain references for the normalized EMG signals. For the ES muscle, the participants lay prone and performed trunk extension with maximum effort against manual resistance applied to the shoulders [37]. For the UT muscle, the participants performed symmetrical shoulder elevation against manual resistance with maximum force [38]. Each MVC trial lasted for 5 s, with a 2 min rest period between trials. The highest 3 s average of the rectified EMG signals during MVC trials was used for normalization (%MVC).
The participant’s subjective workload for repetitive lifting tasks was assessed using the NASA Task Load Index (NASA-TLX) [39,40], which measures scores ranging from 0 (very low) to 100 (very high) across six dimensions: mental demands, physical demands, temporal demands, performance, effort, and frustration. The weights were then determined from each participant’s selections of the subscales that they considered most relevant to workload, based on 15 paired comparisons generated from the six subscales. Overall, participants chose effort as the most relevant (mean = 3.3) and frustration as the least (mean = 0.9). The ratings for each subscale were multiplied by the corresponding weights to obtain a weighted overall workload score.

2.8. Procedure

The experiment consisted of four sequential phases: (1) physical measurement and electrode placement, (2) MVC measurement, (3) task familiarization, and (4) the main experiment. During the familiarization phase, participants received explanations from the experimenters about the correct use and posture associated with the BSE for approximately 20 min and became accustomed to the fit of the device. They then performed about 15–20 lifting movements at their own pace to become familiar with the experimental task of repetitive lifting, followed by practice trials simulating the interruption condition (i.e., digit subtraction). Conditions with and without the BSE were each repeated three times under the three interruption-frequency conditions, resulting in a total of six trials. The order of these six trials was counterbalanced across participants using a Latin square design to minimize order effects. A 60 s rest period was provided between trials to minimize fatigue and learning effects. The entire experimental session lasted approximately 2 h.

2.9. Data Analysis

The data analysis platform used was Labchart 8 software and a custom-written Python script (version 3.10), including NumPy, pandas, and SciPy. The sensor-to-box distance values measured by the distance sensing system during each instance when participants placed the box on the upper shelf were extracted using Python’s Scipy library function, “scipy.signal.find_peaks.” The function was configured with a minimum data distance of 20 samples, a height threshold of mean + 0.15 × SD of the distance data, and a minimum prominence equal to the SD. All automatically detected peaks were cross-checked by the experimenters to verify their correspondence with actual lift placements, resulting in a high agreement (96%) between the automatic and manual detections. For the performance during the lifting task, the number of lifts completed within 100 s of repetitive lifting tasks under each condition was measured by the number of peaks. Given that the placement point on the upper shelf of the box was marked 60 cm from the sensor, the sensor was expected to record a distance of 40 cm if the center of the 40 cm wide box was precisely aligned with the designated point. Accordingly, the placement deviation for each lifting task was defined as the absolute difference between 40 cm and the actual measured distance. To evaluate box placement performance, the average and SD of placement deviations within a trial were computed to represent placement accuracy and variability, respectively.
The onset of each lifting movement, defined as the moment the box was lifted from the lower shelf, was identified using built-in accelerometer signals from the Trigno sensor placed on the ES muscle. The average rectified value of EMG amplitudes during the 1 s period following each onset was then extracted.
To ensure data reliability, EMG data from the first lift of the pre-interruption phase and the last lift of the post-interruption phase were excluded from the analysis. Subsequently, to examine the effects of the interruptions, the mean rectified EMG amplitudes of the last two consecutive lifts during the post-interruption phase (post_EMG (%MVC)) were analyzed. The change in muscle activation (ΔEMG (%)) was calculated by subtracting the mean rectified EMG amplitudes of the first two consecutive lifts during the pre-interruption phase from those of the last two lifts in the post-interruption phase (ΔEMG (%) = post_EMG (%) − pre_EMG (%)).

2.10. Statistical Analysis

Two-way repeated-measures analysis of variance (ANOVA) was performed to investigate the effects of BSE (no BSE and BSE worn during the lifting task) and frequency of interruption (no, intermittent, and frequent) on the variables. The Shapiro–Wilk test confirmed the normality of all data, and Mauchly’s test was conducted to check if the sphericity of the data had been violated. Post hoc analyses using Bonferroni correction for multiple comparisons were conducted. Statistical analyses were performed using SPSS Statistics 24.0 (IBM, Research Triangle Park, NC, USA), with a significance level set at α = 0.05. Data were presented as mean ± SE. The effect sizes are given as partial eta squared (η2), with values of 0.01, 0.06, and 0.14 representing small, moderate, and large effects, respectively [41].

3. Results

The accuracy of the interrupting task showed that participants achieved an overall mean calculation accuracy of 84.2 ± 5.2% in the digit subtraction task (one-digit: 90.1 ± 3.7%; two-digit: 70.5 ± 9.3%).
In the familiarization session, three participants encountered issues while maintaining trunk joint stabilization using BSE. Consequently, these participants were excluded from the experiment. Additionally, the UT muscle data from one participant were identified as outlier compared to the data from the other participants and therefore excluded from the data analysis.

3.1. Muscle Activation

Analysis of the ES muscle indicated that BSE showed lower post-EMG of the ES muscle compared to the no-BSE condition (F(1,9) = 7.2, p < 0.05, η2 = 0.44) (Figure 3a). No significant main effects of interruption frequency or two-way interactions were found. Interruption frequency showed a trend toward significance on ΔEMG of the ES muscle (F(2,18) = 2.9, p = 0.08, η2 = 0.24), and higher muscle activation in the frequent interruption condition compared to no interruption, although this was not significant following Bonferroni correction (p = 0.14) (Figure 3b).
Analysis of the UT muscle data (Figure 4a) indicated no significant main effect of post-EMG of the UT muscle for either BSE or interruption frequency. However, a significant two-way interaction was observed (F(2,16) = 4.3, p < 0.05, η2 = 0.35). Follow-up simple effects analysis revealed that BSE significantly reduced post-EMG compared to no BSE in the no-interruption condition (p < 0.05), whereas the differences between the other pairs remained non-significant. On the other hand, BSE showed a marginally greater ΔEMG of the UT muscle compared to no BSE (F(1,8) = 4.7, p = 0.06, η2 = 0.37) (Figure 4b).

3.2. NASA-TLX Ratings

BSE significantly reduced subjective workload ratings compared to no BSE (F(1,9) = 25.2, p < 0.01, η2 = 0.74) (Table 1a). Interruption frequency also showed a significant main effect (F(2,18) = 8.8, p < 0.01, η2 = 0.49), with higher ratings observed in the frequent interruption condition compared to the no-interruption condition following Bonferroni correction (p < 0.05). No significant two-way interaction was observed.

3.3. Lifting Performance

For the number of lift repetitions, BSE indicated a significantly lower number of lifts compared to no BSE (F(1,9) = 5.1, p < 0.05, η2 = 0.36) (Table 1b). The main effect of interruption frequency and the two-way interaction were not significant. For placement performance, both accuracy and variability were not significantly affected by the combination of BSE and interruption frequency (Table 1c,d).

4. Discussion

We investigated the effects of cognitive interruptions during repetitive low-load lifting tasks while wearing a BSE by examining task performance and muscle activation in the relevant joints. Our data showed that BSE use reduced muscle activation of the ES but increased EMG activation of the trapezius over time and had a lower number of lifting repetitions. Regardless of BSE use, frequent interruptions led to higher perceived exertion than in the no-interruption condition. Box placement performance was not significantly affected by BSE use or interruption frequency.

4.1. Task Performance

The number of lifts is considered as an indicator of lifting endurance and is often used to represent an individual’s physical capacity until fatigue [42,43,44]. Our results demonstrated an 8% reduction in lifting repetitions when wearing the BSE. This finding contrasts with those of previous studies involving lifting tasks with loads ranging from 5 kg to 45 kg, where BSE use was associated with either an increase in lifting repetitions [42] or no significant differences [43]. We speculate that the reduced lifting repetitions may be related to the relatively low lifting load used in this study (≈7% of body weight, 5.3 kg on average), which falls within the light-load range criteria [42] and represents the lower boundary for effective BSE utilization. In addition, the passive BSE employed in this study (4.2 kg) was considerably heavier than those reported in previous studies (1.2 kg in [45]; 0.25–1.28 kg in [46]), suggesting that the added mass concentrated around the waist might have acted as a secondary physical load during repetitive lifting. Although the BSE used in this study has demonstrated its effectiveness in reducing erector spinae EMG activity in previous investigations [47,48], the current study employed a distinct protocol involving continuous, repetitive lifting for 100 s under light-load conditions. During this repetitive lifting, the additional mass of the BSE concentrated around the waist may have been perceived as a secondary physical load over time, potentially contributing to the reduced lifting count observed. Therefore, our findings provide empirical evidence that, while BSE use under low-load conditions remains effective in decreasing erector spinae activation, it may simultaneously attenuate task performance metrics such as lifting frequency over the long term.
In this study, we did not observe a significant effect of BSE use or interruption frequency on task performance. This is probably because the interruption task (one- or two-digit number subtraction) was relatively low in complexity and could be completed within just a few seconds. While several studies have suggested that interruptions can impair task accuracy [11,13], such effects may vary depending on the nature of both the interrupting and the primary tasks [6].

4.2. Muscle Activation of Erector Spinae and Subjective Workload

Our findings indicate that BSE reduced both ES EMG amplitude and subjective workload, even during the repetitive lifting of low-load objects over a 100 s duration. Moreover, the NASA-TLX scores increased with interruption frequency, regardless of BSE use. This aligns with recent research suggesting that cognitive interruptions can negatively affect the maintenance of proper working posture [49]. In real industrial environments, inopportune interactions, such as those between coworkers or smart devices, are common [5], and the complexity and unpredictability of these interruptions may further intensify workloads [6]. Our results suggest that frequent external interruptions may disrupt the internal attention required to sustain a repetitive lifting posture. This is supported by a previous study [50], which showed that during repetitive stoop lifting tasks, when performed in conjunction with cognitively demanding internal interruptions (e.g., random number addition and recall), participants exhibited increased peak upper limb joint moments and forces.
Furthermore, while our results confirm that BSE use generally reduces both physical and cognitive workload compared to the non-BSE condition, they also suggest that under cognitively demanding conditions, such as with frequent interruptions, BSE does not maintain muscle activation and perceived workload at the same reduced levels as in the uninterrupted condition, nor does it provide an additional beneficial effect. This highlights a partial limitation of exoskeletons in mitigating combined physical and cognitive demands, possibly owing to mechanical factors such as restricted range of motion and structural rigidity [51]. In other words, although the BSE provides supportive torque to the lumbar spine, sustaining its workload reduction effects may require not only controlling the physical aspects of the lifting task but also maintaining the user’s internal attention necessary for coordinated use of the BSE. From a practical standpoint, these findings underscore that in intelligent industrial environments, physical assistance alone may not ensure effective workload reduction if cognitive demands remain high. Therefore, when introducing BSEs, workplace designers and system engineers should also incorporate interruption management strategies, such as minimizing unnecessary alerts, scheduling cognitive tasks before or after physical work cycles, and adapting system notifications through timing control [52], to fully realize the benefits of exoskeletal assistance in real work settings.
In this study, participants were ergonomically trained in proper lifting techniques and completed a familiarization session with BSE prior to performing the repetitive lifting task. As a result, they may have been more adept at maintaining posture and effectively utilizing the device. However, in less-experienced users or individuals with lower working memory capacity, cognitive interruptions may impose additional attentional demands for both BSE adaptation and interruption processing, which could lead to increased workload and potential declines in performance metrics, such as task time cost or accuracy [53,54].

4.3. Muscle Activation of Upper Trapezius

Interestingly, our data revealed a contrasting UT activation pattern. As reported in previous studies, UT activation was either unaffected by BSE or slightly reduced under lifting conditions with BSE [55,56]. However, our results showed that the changes in UT muscle activation increased progressively over time when BSE was worn. This is likely because the exoskeleton used in this study was worn in a vest-like configuration covering the shoulders, potentially producing a rucksack-like effect. Given the relatively heavy weight of the BSE and the repetitive 100 s lifting protocol, the sustained load on the shoulders may have contributed to the progressive increase in UT activation. This pattern aligns with previous findings showing that prolonged rucksack use elevates trapezius activation [57] and may reflect time-dependent muscle fatigue in the UT region caused by the continuous support of both the BSE and the lifted loads, although alternative explanations such as changes in lifting coordination cannot be considered due to the absence of kinematic data.

4.4. Limitations

In this study, participants performed a repetitive box placement task toward a single fixed target as the primary task. However, variations in participants’ positions along the coronal plane or trunk rotation were not considered. Consequently, a ceiling effect may have occurred in the measurement of placement accuracy and variability. This interpretation is supported by our results, which showed low placement error (approximately 1.2 cm) and minimal variability (approximately 0.7 cm).
Furthermore, regarding the interruption task, which showed a relatively high mean accuracy of 84%, future investigations should employ more cognitively demanding and ecologically valid interruption types within intelligent systems that may impose greater working memory loads and better simulate real-world distractions. When designing such tasks, researchers should also consider individual differences in working memory capacity and fluid intelligence, as these factors may moderate task difficulty and performance outcomes [58].
Additionally, although the present study measured EMG and task performance, it did not include a kinematic assessment. Consequently, postural changes induced by BSE use or interruption could not be captured. Future studies should incorporate kinematic measurement tools such as motion capture systems or goniometers to analyze postural adaptations during the tasks more precisely.
Lastly, only male participants with similar body sizes were recruited to maintain uniform fit conditions and minimize frequent size adjustments of the BSE, as the fit between the wearer and the device can substantially influence task performance. Nevertheless, future studies should include female participants to verify the generalizability of the present findings.

5. Conclusions

This study examined the effects of cognitive interruption on repetitive lifting in the presence or absence of a BSE, with a focus on task performance and muscle activation. The findings demonstrated that while the BSE effectively reduced ES muscle activation and subjective workload, it also led to an increase in UT activation over time, possibly due to postural constraints and sustained upper-limb support. Furthermore, despite the well-known effectiveness of BSEs, the weight of the handled objects should be carefully selected to ensure optimal assistance and avoid unnecessary strain. Regardless of BSE use, frequent interruptions significantly increased perceived workload and were associated with a marginal increase in ES EMG amplitude, suggesting that cognitive demands can elevate physical strain during repetitive lifting in intelligent work systems.
Importantly, task performance metrics such as box placement accuracy and lifting repetition count were not substantially improved by BSE use under interrupted conditions, indicating the limited additional benefits of BSEs in cognitively demanding environments. These findings suggest that maximizing the benefits of BSEs in intelligent industrial systems requires not only biomechanical assistance but also work designs that support users’ attentional regulation; for example, adaptive pacing protocols or optimized alert mechanisms to manage frequent interruptions could further enhance safety and performance by dynamically adjusting assistance levels based on users’ cognitive state and workload, thereby mitigating the dual-task interference observed in this study.

Author Contributions

Conceptualization, J.C.; methodology, J.C., J.P. and C.P.; software, J.C.; validation, J.C.; formal analysis, J.C., J.P. and C.P.; investigation, J.C.; resources, J.C.; data curation, J.C.; writing—original draft preparation, J.C.; writing—review and editing, J.C.; visualization, J.C.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Dong-A University Research Fund.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Dong-A University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BSEBack-support exoskeleton
EMGElectromyography
ESErector spinae
MVCMaximal voluntary contraction
UTUpper trapezius

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Figure 1. Experimental setup on the upper shelf for the lifting task: (a) a distance-sensing system comprising proximity sensors and an Arduino and (b) visual alignment markers placed on both the box and the shelf to ensure accurate placement.
Figure 1. Experimental setup on the upper shelf for the lifting task: (a) a distance-sensing system comprising proximity sensors and an Arduino and (b) visual alignment markers placed on both the box and the shelf to ensure accurate placement.
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Figure 2. Illustration of task interruption via digit subtraction. (a) The digit subtraction task was initiated by an auditory beep, presented for 3 s, followed by a screen blackout lasting either 7 or 17 s, depending on the specified interruption condition. (b) A monitor and speaker used for task interruption were placed to the left of the experimental rack.
Figure 2. Illustration of task interruption via digit subtraction. (a) The digit subtraction task was initiated by an auditory beep, presented for 3 s, followed by a screen blackout lasting either 7 or 17 s, depending on the specified interruption condition. (b) A monitor and speaker used for task interruption were placed to the left of the experimental rack.
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Figure 3. Normalized erector spinae muscle activation (%MVC) represented as bar plots with individual data points (N = 10) for (a) the post-interruption phase and (b) changes in activation (ΔEMG), across three interruption frequencies (No, Intermittent, Frequent) and two BSE conditions (No BSE: orange; BSE: green).
Figure 3. Normalized erector spinae muscle activation (%MVC) represented as bar plots with individual data points (N = 10) for (a) the post-interruption phase and (b) changes in activation (ΔEMG), across three interruption frequencies (No, Intermittent, Frequent) and two BSE conditions (No BSE: orange; BSE: green).
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Figure 4. Normalized upper trapezius muscle activation (%MVC) represented as bar plots with individual data points (N = 9) for (a) the post-interruption phase and (b) changes in activation (ΔEMG), across three interruption frequencies (No, Intermittent, Frequent) and two BSE conditions (No BSE: orange; BSE: green).
Figure 4. Normalized upper trapezius muscle activation (%MVC) represented as bar plots with individual data points (N = 9) for (a) the post-interruption phase and (b) changes in activation (ΔEMG), across three interruption frequencies (No, Intermittent, Frequent) and two BSE conditions (No BSE: orange; BSE: green).
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Table 1. Results of (a) NASA-TLX score, (b) number of lift repetitions, (c) placement accuracy, and (d) placement variability for back-support exoskeleton use under different interruption frequencies.
Table 1. Results of (a) NASA-TLX score, (b) number of lift repetitions, (c) placement accuracy, and (d) placement variability for back-support exoskeleton use under different interruption frequencies.
Back-Support
Exoskeleton		WithWithout
Interruption
Frequency		NoneIntermittentFrequentOverallNoneIntermittentFrequentOverall
(a) NASA-TLX score54.3
±4.0		54.9
±4.1		61.3
±5.0		56.9
±3.4 a		61.6
±2.9		67.2
±3.9		68.2
±4.4		65.6
±4.2
(b) Number of
lift repetitions		16.5
±1.0		16.5
±1.0		16.9
±1.0		16.8
±1.0 a		17.6
±1.1		17.5
±0.9		17.5
±0.8		17.3
±0.9
(c) Placement
accuracy		1.2
±0.2		1.3
±0.2		1.2
±0.2		1.3
±0.2		1.3
±0.3		1.1
±0.2		1.1
±0.2		1.2
±0.2
(d) Placement
variability		0.7
±0.1		0.8
±0.1		0.7
±0.1		0.7
±0.1		0.6
±0.1		0.7
±0.1		0.7
±0.1		0.7
±0.1
a denotes significant difference compared with non-BSE condition (p < 0.05).
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Choi, J.; Park, J.; Park, C. Simulated Intelligent-System Interruptions: Effects on Back-Support Exoskeleton Performance and Muscle Activation. Actuators 2025, 14, 555. https://doi.org/10.3390/act14110555

AMA Style

Choi J, Park J, Park C. Simulated Intelligent-System Interruptions: Effects on Back-Support Exoskeleton Performance and Muscle Activation. Actuators. 2025; 14(11):555. https://doi.org/10.3390/act14110555

Chicago/Turabian Style

Choi, Jeewon, Junik Park, and Chaerim Park. 2025. "Simulated Intelligent-System Interruptions: Effects on Back-Support Exoskeleton Performance and Muscle Activation" Actuators 14, no. 11: 555. https://doi.org/10.3390/act14110555

APA Style

Choi, J., Park, J., & Park, C. (2025). Simulated Intelligent-System Interruptions: Effects on Back-Support Exoskeleton Performance and Muscle Activation. Actuators, 14(11), 555. https://doi.org/10.3390/act14110555

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