2.1. Design requirements

The design of the testbed (Figure 1) had to comply with the set of technical and functional requirements listed hereafter, which were partly defined by the Eurobench project promoters and partly derived from the authors’ analysis:

• • compliant with technical and ergonomics norms of stair design;

• • a minimum of five steps to allow the acquisition of two complete stance phases for each limb;

• • variable tread and riser lengths, by changing the staircase inclination to test the exoskeletons in different climbing conditions;

• • two people, for example, the patient and the therapist, must be allowed to climb the staircase side by side, leading to a minimum width of the staircase of $ 1500 $ mm;

• • a stair landing at the top of the staircase to allow the exoskeleton users and, if necessary, their companions to turn around and face the descent;

• • motorized system to change the tread and riser lengths to facilitate and speed up the setup operations;

• • a set of sensors to allow the measurement of kinematic and dynamic data while using the exoskeleton;

• • a set of software tools, algorithms, and metrics to quantitatively analyze the user’s motor capabilities and to provide a comprehensive platform to assess the exoskeleton performances.

Figure 1.

The STEPbySTEP staircase prototype.

They served as a guide for the mechanical design (Section 2.2), the selection and integration of sensors (Section 2.3), metrics definition (Section 3), and protocol design (Section 4).

It has to be specified that there is no specific target user group w.r.t. the testbed. Therefore, the proposed design (including testing protocols and evaluation metrics) can be applied to any participant.

2.2. Mechanical design and assembly

The STEPbySTEP testbed is a reconfigurable five-step staircase with appropriate adjustments to regulate the relative position among the steps and, consequently, the inclination of the staircase.

By referring to the lateral view of the adjustment mechanism depicted in Figure 2, the parallelogram four-bar linkage $ ABCD $ represents the structure of each stair stringer. Let us denote the vertical distance between $ \overline{AB} $ and $ \overline{CD} $ by h, and the total length of the stringer by d. The inclination of the staircase is $ \alpha =\arcsin \left(h/d\right) $ . Each step is constrained on the stringer by two hinges $ {A}_i $ and $ {B}_i $ . Their positions along the stringer are identical to guarantee that each step is parallel to the ground. The distance between two consecutive steps along the stringer is denoted by l. Consequently, the tread length is $ p=l\cos \alpha $ , and the riser length is $ a=l\sin \alpha $ . Therefore, the tread and the riser lengths can be adjusted by modifying h and l.

Figure 2.

Kinematics of the staircase mechanism.

The realized adjusting mechanisms are depicted in Figure 3. The height of the staircase $ h $ is adjusted by a Pantograph lift table (Ameise®HIW 2.0, by Jungheinrich), denoted by actuator for height regulation in Figure 1. Moreover, the distance between two consecutive steps $ d $ is adjusted by modifying the position of two Dowel pins, hinged to each side of a step, along a series of holes present in each stringer.

Figure 3.

Adjustments realized in the prototype.

In order to represent the adjustment capabilities of the staircase, let us refer to the international norm ISO14122-3. It imposes the following conditions for realizing ergonomic staircases in civil works:

(2.1) $$ {b}_l\hskip0.35em \leqslant \hskip0.35em v\hskip0.35em \leqslant \hskip0.35em {b}_u, $$

with

• • $ v=p+2a $

• • $ {b}_l=600\mathrm{mm} $ and $ {b}_u=660\mathrm{mm} $ denote the boundary limits of $ v $ .

Let us define the discrepancy of a generic geometrical configuration w.r.t. the acceptable range imposed by norms (2.1) by:

(2.2) $$ e=\left\{\begin{array}{ll}v-{b}_l,& \mathrm{if}\;v<{b}_l\\ {}0,& \mathrm{if}\;{b}_l\le v\le {b}_u\\ {}v-{b}_u,& \mathrm{if}\;v>{b}_u.\end{array}\right. $$

The colored surface shown in Figure 4 shows the influence of geometrical parameters $ p $ and $ a $ on e. The values $ p $ and $ a $ for which $ e $ is equal to 0 comply with the norm ISO14122-3 (2.1). The gray surfaces represent the relationship between $ p $ and a, given a distance $ l $ between two consecutive steps along the stringer, modifying the inclination $ \alpha $ of the staircase. For clarity of representation, only surfaces with $ l=30 $ , 35, and 40 $ cm $ are represented. Intermediate configurations can be obtained by appropriately adjusting Dowel pins shown in Figure 3. Once $ l $ is set, the values of $ p $ and $ a $ that comply with the norm are given by the intersection line between the colored surface and the corresponding gray surface where $ e=0 $ .

Figure 4.

Discrepancy of staircase configurations concerning limits imposed by the International 14122–3 norm (1).

2.3. Sensors selection

The testbed is provided with integrated sensors and acquisition systems to measure kinematic and dynamic data. The handrails of the testbed are sensorized with four load cells (Forsentek, F3G-500 N), two for each handrail, connected through the PhidgetBridge 4-Input modules bridge interfaces (Phidgets Inc., Canada), to measure the forces applied by the user wearing the exoskeleton (e.g., a patient using one crutch and the sustain of a handrail) during the ascent or descent of the stairs. Two force plates (P-6000, BTS Bioengineering, Italy) can be properly inserted into any of the steps to measure the ground reaction forces, thanks to the movable wooden tiles that allow for flexible positioning. This setup also permits the insertion of two force plates in the same step, which is useful, for example, when studying both the foot placement and the use of a crutch simultaneously. Load cells and force plates are installed as depicted in Figure 1 and in Figure 5.

Figure 5.

Load cells and force plates installed on the staircase.

A motion caption system (Perception Neuron Pro, Noitom Ltd., US) is part of the testbed to measure the user kinematics, available in the installation premises.

3.1. Physical interaction metrics

One way to assess the performance of an exoskeleton is by investigating the user’s motor capability in performing a task while wearing the exoskeleton. This can be done by analyzing physical measurements, such as the muscular activation pattern and interaction forces. This section discusses the developed metrics calculated from the Surface ElectroMyoGraphic (SEMG) signals and interaction forces measured with two force plates installed in the staircase steps and four load cells integrated into the handrails. Using the IMUs to calculate the duration of the gait phases, that is, stance (ST), swing (SW), double support (DS), and gait cycle (GC), the metrics are calculated for each phase.

SEMG is a method to record the electrical muscle activity during contraction; the stronger the muscle contraction, the higher the electrical activity. Two metrics based on SEMG were used: an index to quantify the level of activity of a muscle, the amount of EMG of muscle $ i $ ( $ {AEMG}^i $ ), and an index quantifying the level of co-contractions between two muscles $ i $ and $ j $ ( $ {ccEMG}^{ij} $ ). These metrics have been previously used in studies related to exoskeletons and gait analysis (Roveda et al., Reference Roveda, Haghshenas, Caimmi, Pedrocchi and Tosatti2019). The novelty in our approach lies in applying these metrics to stair climbing, specifically to assess the effect of exoskeleton assistance on the neuromuscular activation pattern across different gait phases.

The significance of $ {AEMG}^i $ and $ {ccEMG}^{ij} $ on gait functionality is supported by previous research, which has demonstrated that increased co-contraction may be associated with joint stiffness and altered gait patterns, particularly in populations with gait impairments (Damiano et al., Reference Damiano, Martellotta, Sullivan, Granata and Abel2000; Latash, Reference Latash2018). Additionally, reduced or optimized muscle activation patterns have been shown to correlate with more efficient gait mechanics, potentially reducing the metabolic cost of walking (Collins et al., Reference Collins, Wiggin and Sawicki2015; Galle et al., Reference Galle, Malcolm, Collins and De Clercq2017). Thus, these metrics indicate the exoskeleton’s ability to assist in achieving a more efficient and stable gait pattern during stair climbing.

Regarding the ground and handrail interaction forces, several metrics were defined for both feet: i) the maximum vertical and sagittal ground reaction force at the beginning stance ( $ {GFz}_1 $ and $ {GFy}_1 $ ) and ii) at push-off ( $ {GFz}_2 $ and $ {GFy}_2 $ ), iii) the maximum vertical and sagittal handrail reaction forces during double support ( $ HFz $ and $ HFy $ ), and iv) the maximum lateral handrail reaction force during double support ( $ HFx $ ). These metrics are used to assess the subject’s capability to: i) accept the load, ii) push off before the swing phase, iii) maintain equilibrium without exerting excessive forces on the handrails in the vertical and sagittal directions, and iv) maintain equilibrium without exerting excessive lateral forces on the handrails.

The correlation between these ground reaction force (GRF) metrics and specific gait functionalities has been well-documented in the literature. For example, maximum vertical GRF during the stance phase is indicative of the body’s ability to support itself and absorb impact, which is often compromised in individuals with gait deficits (Winter, Reference Winter1991; Vaughan et al., Reference Vaughan, Davis and O’Connor1999). Similarly, the push-off phase is crucial for forward propulsion, and deficiencies in this phase are linked to reduced gait speed and efficiency, often observed in elderly or neurologically impaired populations (Schenck and Kesar, Reference Schenck and Kesar2017; Awad et al., Reference Awad, Lewek, Kesar, Franz and Bowden2020; Alingh et al., Reference Alingh, Groen, Kamphuis, Geurts and Weerdesteyn2021). The handrail forces, on the other hand, can indicate a reliance on external support, which may be necessary for maintaining balance and stability during stair ascent and descent, particularly in individuals with compromised motor control (Novak and Brouwer, Reference Novak and Brouwer2014; Stessman et al., Reference Stessman, Rottenberg and Jacobs2017).

3.2. Temporal phases

In stair negotiation, defining sub-phases beyond the basic stance, swing, and double-support phases is essential for a deeper understanding of biomechanics. These sub-phases help capture the finer details of movement dynamics during each part of stair ascent and descent. By segmenting the movement into more specific intervals, such as weight acceptance, push-off, or controlled lowering, it becomes possible to analyze the biomechanical contributions during each part of the stair negotiation process. This level of detail is particularly important when studying complex systems like exoskeletons, which aim to replicate or assist human movement. A more detailed sub-phase framework allows for a nuanced evaluation of performance and movement assistance provided by these systems.

In this paper, we adopt the phases and sub-phases definition proposed by Harper et al. (Reference Harper, Wilken and Neptune2018)), as illustrated in Figure 6. The detailed breakdown of phases provides a structured foundation for analyzing exoskeleton performance in stair negotiation tasks.

Figure 6.

Figure adapted from Harper et al. (Reference Harper, Wilken and Neptune2018)).

The six phases (and their associated events) during ascent are as follows:

• • Stance phase:

  • – Weight acceptance (WA). From ipsilateral foot strike to contralateral toe-off.

  • – Pull-up (PU). From the beginning of single-leg support to the mid-swing of the contralateral leg.

  • – Forward continuance (FCN). From mid-swing of the contralateral leg to the contralateral foot strike.

• • Swing phase:

  • – Push-up (PU). From contralateral foot strike to ipsilateral toe-off.

  • – Swing foot clearance (SFC). From ipsilateral toe-off to mid-swing of the ipsilateral leg.

  • – Swing foot placement (SFP). From mid-swing of the ipsilateral leg to ipsilateral foot strike.

The five phases during the descent are as follows:

• • Stance phase:

  • – Weight acceptance (WA). From right foot contact to left toe-off.

  • – Forward continuance (FCN). From left toe-off (single right-leg support) to mid-swing of the left leg.

  • – Controlled lowering (CL). From mid-swing of the left leg until right toe-off.

• • Swing phase:

  • – Leg pull-through (LP). From right toe-off to mid-swing of the right leg.

  • – Foot placement (FP). From mid-swing of the right leg to right foot contact.

Based on this phase definition, 11 temporal metrics can be established (see Table 1). This protocol aims to achieve two primary objectives: first, to compute the temporal metrics (refer to Section 4.2); and second, to provide the raw kinematic data to the end user, enabling its utilization as deemed appropriate. For each sub-phase, for example, the average angles for the different joints can be calculated, as well as the angle at the end of the sub-phase.

Table 1.

Temporal metrics

3.3. Human factors metrics

Several literature contributions highlight the importance of adopting a user-centered perspective on exoskeleton design (Bush and ten Hompel, Reference Bush and ten Hompel2017). For instance, among the most critical factors for successful gerontechnology acceptance and uptake, we can find usability (Shore et al., Reference Shore, Power, Hartigan, Schülein, Graf, de Eyto and O’Sullivan2019), the possibility to contemporarily perform motor and cognitive tasks (O’Sullivan et al., Reference O’Sullivan, Power, Virk, Masud, Haider, Christensen, Bai, Cuypers, D’Havé and Vonck2015), and comfort (Wolff et al., Reference Wolff, Parker, Borisoff, Mortenson and Mattie2014). Despite this, human factors and human–technology interaction aspects, such as usability and acceptance, are often overlooked when exoskeletons are designed, and there is currently no framework in the literature that allows for an evaluation of the usability and the impact on users’ perceptions and behaviors of exoskeletons about the task of climbing and descending stairs.

The cognitive workload is an important factor to consider when evaluating the performance of exoskeletons, as it can affect the user’s ability to use the device effectively and safely. The dual-task paradigm is commonly used for assessing cognitive workload, as it involves the simultaneous performance of two tasks that differ in their cognitive demands (Shaw et al., Reference Shaw, Rietschel, Hendershot, Pruziner, Miller, Hatfield and Gentili2018). Usability is another key factor. The System Usability Scale (SUS) is a widely used tool for measuring usability. It assesses the user’s overall satisfaction, and it is widely adopted for exoskeleton usability assessment as well (Orekhov et al., Reference Orekhov, Fang, Cuddeback and Lerner2021). Perceived musculoskeletal pressure and discomfort are, as well, crucial factors to consider when evaluating exoskeletons, as they can affect the user’s ability to use the device comfortably and effectively over an extended period. The local perceived pressure method is a tool that can be used to assess the level of pressure or discomfort experienced by the user when using the exoskeleton (Kermavnar et al., Reference Kermavnar, de Vries, de Looze and O’Sullivan2021). Technology acceptance is another crucial factor to consider when evaluating exoskeletons. van der Laan Acceptance Scale is a tool that can be used to assess the user’s acceptance of the technology. The scale measures the user’s willingness to use the technology and the level of confidence they have in its ability to perform the intended tasks (Van Der Laan et al., Reference Van Der Laan, Heino and De Waard1997). The following cognitive metrics are thus selected, encompassing in particular: cognitive workload as measured by an adapted version of the dual-task paradigm (Plummer-D’Amato et al., Reference Plummer-D’Amato, Altmann, Saracino, Fox, Behrman and Marsiske2008); usability as measured by the System Usability Scale (Lewis and Sauro, Reference Lewis and Sauro2017); perceived musculoskeletal pressure/discomfort as measured by the local perceived pressure method (Hamberg-van Reenen et al., Reference Hamberg-van Reenen, van der Beek, Blatter, van der Grinten and van Mechelen2008), and technology acceptance as assessed by van der Laan Acceptance Scale (Van Der Laan et al., Reference Van Der Laan, Heino and De Waard1997).

4.1. Physical interaction protocol

It is based on the interaction metrics; the rectus femoris (RF) and the biceps femoris (BF) muscles were selected as showcases as highly involved in the task. More muscles should be studied to have a picture of the neuromuscular activation pattern underneath the movement.

The setup is as follows:

• • four-EMG channels (four probes of the Delsys system) to assess bilaterally the activity of the selected muscles;

• • two IMUs (two probes of the Delsys system) are placed at the shanks to identify the gait phases (Salarian et al., Reference Salarian, Russmann, Vingerhoets, Dehollain, Blanc, Burkhard and Aminian2004);

• • two force plates were inserted in the second and third steps to evaluate a complete gait cycle; and

• • two load cells integrated into one of the handrails to measure the interaction forces.

Data acquisition protocol:

1. 1. User preparation: donning of the exoskeleton and EMG electrode placement.

2. 2. Resting and standing acquisition: resting acquisition with the subject sitting on a chair.

3. 3. Place the subject at a one-step distance from the first step of the stairway.

4. 4. Start data acquisition: launch the data collection software.

5. 5. Ascending stairs: at the go command, the subject makes a left footstep and climbs the stairs, beginning with the right foot, till reaching the upper platform (usually, subjects use the support of a crutch and the handrail).

6. 6. Stop the acquisition when the user stands with two feet on the upper platform.

7. 7. Turning: the subject turns and prepares to descend the stairs.

8. 8. Descending stairs.

9. 9. Trials repetitions: steps 3–7 are repeated (four other trials).

10. 10. Change the staircase inclination: the operator changes the staircase inclination using the platform elevator.

11. 11. Second set of trials: steps 3–7 are repeated (five trials).

The execution of the protocol, donning the exoskeleton excluded, lasts 30–40 min. Metrics are calculated offline with the software provided to Eurobench.

4.2. Temporal phases protocol

The protocol aims to define the phases of stair negotiation during both ascent and descent, facilitating a detailed understanding of human movements in these contexts. To achieve this, a subset of sensors from a motion capture (mocap) system is utilized, specifically tailored for the knee joint angle calculation and phase definition.

The setup consists of the Perception Neuron Pro system (Perception Neuron Pro, Miami, US), which uses inertial measurement units (IMUs) for capturing kinematic data.

The protocol is defined as follows:

1. 1. Place the mocap system onto the subject’s body (focusing on the lower limbs). The placement is optimized based on the geometric constraints of each exoskeleton and the recommended locations provided by the capture system manufacturer, allowing for flexibility in sensor positioning.

2. 2. Perform mocap calibration to ensure accurate data capture.

3. 3. Position the subject at the starting point, which is one step away from the stairway.

4. 4. Launch the data collection software to begin recording.

5. 5. Conduct the experiment, where the subject ascends and descends the stairs using a step-over-step approach.

6. 6. Stop the data collection once the subject returns to the initial position.

7. 7. Repeat the procedure five times to ensure reliability and consistency in the data.

The total of five runs takes approximately 22 min.

For the gait events and phase identification, only knee joint angle data have been used (Figure 7). Looking at the figure, the differences between exo and no-exo are particularly noticeable in the three peaks during the ascending phases. These peaks indicate a slightly more rigid and less fluid movement in the exo case, suggesting that the knee does not rotate as much as it does without the exoskeleton.

Figure 7.

Knee joint angle of a healthy subject during stair ascent and descent, with (left) and without (right) a lower limb exoskeleton (LLE).

The algorithm used to identify gait events and phases from knee angle data is based on the K-nearest-neighbors (KNN) machine learning (ML) algorithm, with results shown in Figure 10). KNN is a supervised classification method that identifies patterns in data by determining the category of a data point based on its proximity to other, already categorized points. In the context of gait phase identification, the KNN algorithm classifies IMU data into different gait phases by comparing the current data to a previously labeled training dataset.

Two separate ML models were trained: one for stair ascent and another for descent. The training dataset consists of 34 trials from five participants (three females and two males) without exoskeletons. The participants’ mean age was 31.4 years (SD: 7.27), their mean weight was 75.6 kg (SD: 4.39), and their mean height was 1.71 m (SD: .09). The first three participants completed six trials each, while the last two completed eight trials. The trained algorithms were subsequently tested using an exoskeleton, and the results are presented and discussed in Section 5.

This protocol not only enables the calculation of phases and sub-phases but also allows for the assessment of joint angles using IMUs (Recinos et al., Reference Recinos, Abella, Riyaz and Demircan2020). Additionally, it is compatible with other signal acquisition systems, such as EMG and GRF. This combination offers a more comprehensive analysis of biomechanical performance, extending beyond traditional phases like stance, swing, and double support. In this paper, we focus specifically on the definition of temporal phases.

4.3. Human factors protocol

Human factors metrics are assessed through an adapted version of the dual-task paradigm – applied to measure cognitive workload – and through various questionnaires and scales measuring usability, acceptance, and discomfort – administered at the end of the task. Regarding the cognitive workload, this protocol emulates cognitively demanding situations, as participants are asked to simultaneously perform the STEPbySTEP testbed task (going up and down the stairs continuously) and a sound recognition task. The latter consists of presenting a sequence of sound stimuli (phonemes) to the participants, who have to say either “no” (each time the phoneme is different from the previously listened one) or “yes” (each time the phoneme is the same as the previous one). To discern differences in cognitive performance due to the exoskeleton design characteristics, every participant must perform the task under two conditions: the control condition (without wearing the exoskeleton) and the experimental condition (while wearing the exoskeleton). The control condition is necessary since the dual-task paradigm allows the cognitive workload to be assessed by calculating the difference in the number of errors and latency of responses between the single-task baseline (only the sound recognition task) and the dual-task condition (when adding exoskeleton-assisted movement). Therefore, analyzing differences in the cognitive workload levels between the two conditions allows us to discern the cognitive load generated by wearing the exoskeleton alone. Higher latency and higher number of errors correspond to higher levels of cognitive workload (Koch et al., Reference Koch, Poljac, Müller and Kiesel2018).

In other words, our protocol envisages conducting a cognitively demanding task (the sound recognition task) in two different conditions: without wearing the exoskeleton and with the exoskeleton on. Since the sound recognition task does not change between the two conditions, and the only feature that changes between the two conditions is the presence of the exoskeleton, we can infer that changes in cognitive load are caused by the exoskeleton design. To minimize bias, participants were trained to familiarize themselves with the use of the exoskeleton. The STEPbySTEP testbed task (going up and down the stairs continuously) is performed in both conditions to ensure that participants are actively interacting with the exoskeleton. Therefore, analyzing differences in the cognitive workload levels between the two conditions allows us to discern the cognitive load generated by wearing the exoskeleton alone. Higher latency and higher numbers of errors correspond to higher levels of cognitive workload (Koch et al., Reference Koch, Poljac, Müller and Kiesel2018).

W.r.t. the human factors questionnaires, usability is measured through the 10-item System Usability Scale (Lewis and Sauro, Reference Lewis and Sauro2017), a 5-point Likert scale going from 1 = strongly disagree to 5 = strongly agree. Example item: “I think that I would like to use this exoskeleton frequently.”

Acceptance is measured using the 9-item van der Laan Acceptance Scale (Van Der Laan et al., Reference Van Der Laan, Heino and De Waard1997). Participants are asked to rate the exoskeleton on a 5-point semantic differential scale with two opposite adjectives (i.e., useless–useful). The score ranges from −2 (negative opposite) to +2 (positive opposite). Four items related to the usefulness sub-dimension (i.e., “I found the XSPINE prototype to be … Effective – Superfluous”), while five items to the satisfaction sub-dimension (i.e., “I found the XSPINE prototype to be … Pleasant – Unpleasant”).

Discomfort is measured through a modified version of the 12-item Local Perceived Discomfort Scale (Hamberg-van Reenen et al., Reference Hamberg-van Reenen, van der Beek, Blatter, van der Grinten and van Mechelen2008) that includes lower limbs. Participants are asked to rate to which extent the exoskeleton exerted pressure on different body parts on an 11-point scale (0 = no pressure at all to 10 = maximal pressure).

In both conditions, an experimenter registers the participant’s responses manually. Data are collected through the “OpenSesame” software, which records the audio and the participants’ number of errors and response times. Moreover, participants are asked to fill out usability, acceptance, and discomfort scales through an online questionnaire on “Qualtrics” at the end of the task.

The summary of the protocol is detailed below:

1. 1. A detailed explanation of the experiment and the two phases of the task;

2. 2. Earphones wearing and volume calibration;

3. 3. Running “OpenSesame” software;

4. 4. Place the subject at the starting point;

5. 5. Trial run: perform the dual task;

6. 6. Addressing participant’s eventual doubts and questions, eventual volume re-calibration, and “OpenSesame” re-running;

7. 7. Experimental run: perform the dual task;

8. 8. Donning the exoskeleton;

9. 9. Repeat the procedure described from step 3 to step 8 (with the participant wearing the exoskeleton);

10. 10. Doffing the exoskeleton;

11. 11. Saving audio recordings and data collected through “OpenSesame” (i.e., response time in milliseconds and error number);

12. 12. Ask participants to fill out the Local Perceived Discomfort Scale (Hamberg-van Reenen et al., Reference Hamberg-van Reenen, van der Beek, Blatter, van der Grinten and van Mechelen2008) to assess discomfort;

13. 13. Administration of the System Usability Scale (Lewis and Sauro, Reference Lewis and Sauro2017) and van der Laan Acceptance Scale (Van Der Laan et al., Reference Van Der Laan, Heino and De Waard1997) to assess usability and acceptance.

The total time to perform the human factors protocol (for one subject) is around 20 min.