Sales, P. Fabregat and A. Beccai, S. Micera, C. Cipriani, J. Carpaneto and M. Roccella, E. Cattin, N. Vitiello, F. Vecchi and M. Tsagarakis, D. Caldwell and S. Moreno, E. Turowska and J. Turouska and J. Bastos-Filho, M. Sarcinelli-Filho, A. Ferreira, W. Celeste, R. Silva, V. Martins, D. Cavalieri, P. Filgueira and I. Undetected location. NO YES. Home Subjects Nanotechnology General. Wearable Robots: Biomechatronic Exoskeletons. Selected type: Hardcover. Added to Your Shopping Cart. View on Wiley Online Library. This is a dummy description. A wearable robot is a mechatronic system that is designed around the shape and function of the human body, with segments and joints corresponding to those of the person it is externally coupled with.
Teleoperation and power amplification were the first applications, but after recent technological advances the range of application fields has widened. Increasing recognition from the scientific community means that this technology is now employed in telemanipulation, man-amplification, neuromotor control research and rehabilitation, and to assist with impaired human motor control.
What the apprentice lacks in wearable robotic specific material it more than makes up in its wonderful insights into multi-disciplinary research. The author heavily emphasizes how many inventions started out as rehabilitation technology just like exoskeletons only to balloon out into everyday life. Every time I talk to rehabilitation professionals the conversation veers into specific muscle groups within the first minute. Strength Training Anatomy is an atlas that gives you the location, name and function of all major skeletal muscle groups in the body.
This knowledge is also essential when looking at mathematical 3D models that attempt to approximate the impact of exoskeletons on the human body, see example AnyBody Simulation.
Finally, multiple soft exoskeleton designs are experimenting with applying forces along the length and in parallel to skeletal muscles, rather than just torque at a joint. Having a handy picture guide in the form of this or similar books can be quite useful for any exoskeleton technology enthusiast.
You can pick up this book or its new 3rd edition at Amazon. This book is meant to act as a textbook that positions students to enter the wearables market. Unfortunately, the first half of the book ends up reading like a loosely agglomerated Wikipedia articles. This book is filled with rehabilitation exoskeleton devices. There are some lower extremities exoskeletons but the main focus is clearly in full arm and below the wrist only rehabilitation wearable robots.
However, time has not been a friend to this volume. While the text was printed in , you will be hard pressed to find much material before Without a technological overview section or many forward looking statements this text begins to resemble a graveyard of obscure and forgotten research projects that were terminated once the funding ran out.
Saving the worst for last, there is only one thing you need to know about this book, SKIP! In order to assess the inter-session variability, we compared the MVC values acquired in each of the three sessions, and the kinematics and the muscle activity related to the three conditions in which the exoskeleton was used in passive modality. For this purpose, one repetition was chosen for day 1 and day 3. No significant differences were found in the MVC values between the first and the second session, and across the three sessions.
Additionally, no significant differences were found in the performance of the movements across the three days Fig. Movement execution and muscle activity across the three sessions, while performing the reaching tasks wearing the exoskeleton in passive modality day 1 WEP1 , day 2 WEP2 , and day 3 WEP3. Dark red, light red, and purple lines code day 1, day 2, and day 3, respectively.
The maximum value for the nMD and nPK is reported in the upper right corner of each plot. The mean and the standard errors refer to the three subjects and one repetition. On the x-axis, the duration of the movement is represented in percentage and it includes the forward and backward movement. On the bottom, the Pearson correlation coefficients R joint and the angular distance d joint in deg are reported for each target.
Each value represents the mean across the three subjects. Dark red lines code the average correlation between the day 1 and day 2 and between day 1 and day 3, light red lines code the average correlation between day 2 and day 1 and between day 2 and day 3, and purple lines code the average correlation between day 3 and day 1 and between day 3 and day 2. The maximum value for the distance and the correlation is reported in the upper right corner of each plot.
On the x-axis the duration of the movement is represented in percentage, and it includes the forward and backward movement. The timing and the level of the muscle activity were generally preserved among the three sessions for all muscles average RMS EMG difference across sessions and muscles: 0. Overall, the results show that the movement execution and the muscle activity were very similar for the three days; consequently, the results can be reasonably compared across the different sessions.
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In this regard, in the following analysis, we used the movements performed in passive modality in day 1 as comparison for the free movements and the assistive modality and the movements performed in passive modality in day 3 for assistive joint and EE controls. The EE kinematics during the reaching tasks was slightly modified by using the exoskeleton Fig. The Pace and the nPK , instead, were comparable between the two conditions, albeit free movements showed a trend of higher smoothness nPK.
Dark blue and red lines code the free movements and the passive modality, respectively. On the bottom, the p-values, Bonferroni corrected for the number of targets, related to the comparison of free movements and passive modality are reported in a gray scale for each target. The mean and the standard errors refer to six subjects and three repetitions. Blue, dark red, and green lines code the free movements, the passive modality, and the assistive modality, respectively.
On the right, the Pearson correlation coefficients R joint in the first column and the angular distance d joint in the second column in deg are reported for each target. Each value represents the mean across six subjects and three repetitions. Blue lines code R joint and d joint between free movements and passive modality. Green lines code R joint and d joint between passive and assistive modality. The maximum value for R joint and d joint is reported in the upper right corner of each plot. The joint angular excursions were very similar between passive and assistive modality, but they differed from the condition without the exoskeleton Fig.
Instead, the abduction of the shoulder, especially for the targets in the North direction see d joint of SH-abd in targets 1, 2, 10, 11, and 12 , and the rotation of the shoulder for the targets in the West direction see d joint of SH-rot in targets 8, 9, and 10 were reduced. Moreover, wearing the exoskeleton increased the flexion-extension of the shoulder for the targets in the North direction see d joint of SH-flx in targets 1, 2, 11, and 12 , and the flexion of the elbow, especially for the target in the North and South direction see d joint of EL-Flx in targets 4, 5, 6, 7, 8, and Concerning the differences between active and passive movements with ALEx, it is possible to notice that in the assistive modality the SH-Abd and SH-Rot tended to be stabilized with a reduced angular excursion, while the movements were mainly achieved with the modulation of the flexion-extension of the shoulder and elbow.
Finally, it is also possible to notice that the subjects in the passive modality tended to anticipate the maximum extension-flexion of the upper limb see SH-Flx and EL-Flx with respect to the movements controlled by ALEx. The LAT, in free movements, and the shoulder elevator muscles, in both conditions free movements and passive modality , were active during the whole reaching movement. The mean values refer to six subjects and three repetitions. The duration of each movement is represented in percentage, and it includes the forward and backward movement.
On top of the RMS EMG plot, the p-values, Bonferroni corrected for the number of targets, between free movements and passive modality top row and between passive and assistive modality bottom row for the twelve targets are reported according to a gray scale. Each spinal map is the average among the maps of six subjects and three repetitions. On the x-axis the duration of the movement is represented in percentage and it includes the forward and backward movement.
The black lines represent the averaged CoA. Dark blue lines code the comparison between free movements and passive modality. Dark green lines code the comparison between passive and assistive modality. The maximum value for the correlation and the distance is reported in the upper right corner of each plot. As already reported in [ 21 ], wearing the exoskeleton induced a redistribution of muscle contribution for the execution of the reaching task: the control of the shoulder and of the elbow extension in free movements was substituted by the control of the back muscles and of the elbow flexors.
The higher activity was located in the lower cervical and in the upper thoracic segments for the targets North and East, and primarily in the cervical and less in the upper thoracic segments for the South and West targets. The spinal maps when wearing the exoskeleton in passive modality differed from those of free movements mean R Map-EMG for the 12 targets: 0.
Indeed, the activity for North and East targets in passive modality was more similar to the one in the West target for free movements, with a higher involvement of the C5 and C6 segments, while the activity in the South and West directions had a similar location but it was attenuated. Finally, when wearing the exoskeleton in assistive modality, the MN activity was generally similar, but less intense with respect to the passive modality mean R Map-EMG for the 12 targets: 0.
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Muscle synergies analysis was performed to assess possible effects of the use of the exoskeleton on muscle coordination in passive and assistive modality Fig. The mean and standard errors refer to six subjects. Below S1, S2, and S3, the p-values for the weighting coefficients between free movements and passive modality top row and between passive and assistive modality bottom row are reported using a gray scale. On the bottom of the RMS SYN the p-values, Bonferroni corrected for the number of targets, between free movements and passive modality top row and between passive and assistive modality bottom row are reported using a gray scale.
On the bottom of the weighting coefficients, the average DOT SYN for each synergy for the three conditions blue for free movements, dark red for passive modality, and green for assistive modality. On the left, the spatiotemporal organization maps for each muscle synergy in free movements first column , passive modality second column , and assistive modality third column. Each map is the average one among six subjects and three repetitions.
Blue lines code the comparison between free movements and passive modality. Green lines code the comparison between passive and assistive modality. The maximum value for the correlation is reported in the upper right corner of each plot. Four muscle synergies, similar to those reported in our previous work [ 21 ], were extracted for each subject for free movements 3. During free movements, muscle synergies were similar to those already reported in literature for an analogous task [ 31 ]. In particular, S1 mainly grouped the muscles dedicated to the flexion-extension and abduction-adduction of the shoulder i.
S2 accounted for the muscles responsible for the shoulder elevation i. Moreover, it was mainly located between C6 and C8. It was mainly active in the forward movement, in particular on the midway between the starting and the target position, and it was mainly located in C3 and C4. Despite the preservation of the structure, the spatiotemporal activation of the shared muscle synergy changed across conditions.
S1 was characterized by three bursts of activity with a similar intensity and located in the same segments for free movements and passive modality, while in the assistive modality the level of activity was slightly lower. The spatiotemporal organization of S3 highly varied across conditions the average R Map-SYN over the 12 targets was 0.
Finally, wearing the exoskeleton in the passive modality favored the activation of two additional muscle synergies S5 and S6 that substituted the activation of S4. The statistical analysis did not show any significant difference in the movement execution and in the muscle activity and coordination between assistive modality with joint and EE control, but a trend and some differences were evident between the two conditions. The movement execution and muscle activity in the third session.
The mean values refer to three subjects and three repetitions. Dark cyan lines code the R joint and d joint between passive and assistive modality with joint control. Dark yellow lines code the R joint and d joint between passive and assistive modality with EE control. The maximum value for the correlation and for the distance is reported in the upper right corner of each plot. The black lines code the mean CoA.
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The mean values for the spinal maps and the CoA refer to three subjects and three repetitions. The mean and standard errors refer to three subjects. In the second column, the RMS SYN is reported for the 12 targets and it corresponds to the average values across three subjects and three repetitions. Dark red, dark cyan, dark yellow lines code the passive modality, the assistive modality with joint control, and the assistive modality with EE control, respectively. In the third column, the spatiotemporal organization of each muscle synergies for passive modality first column , assistive modality with EE control second column , and assistive modality with joint control third column is reported for target North.
Each map is the average among the maps of three subjects and three repetitions. Dark cyan lines code the comparison between passive and assistive modality with joint control. Dark yellow lines code the comparison between passive and assistive modality with EE control. The maximum value for the correlation is reported in the upper right corner of S1.
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As expected, the level of activity of the muscles in the two active modalities was lower for most of the muscles respect than the passive modality. However, the control at the joints promoted a slightly higher muscle activity than the control at the EE in all muscles data not showed. Moreover, the overall upper limb muscle activity for the joint control resulted more similar to that for the passive modality than that for the EE control see the higher R Map-EMG and the lower d COA between passive and assistive modality with joint control in Fig.
For an easy inter-group comparison, five muscle synergies were retained for the passive modality. Five muscle synergies were also found for assistive modality with EE control 4. The level of activation of all the muscle synergies was lower for both assistive modalities with respect to passive modality, but S1, S3, and S5 showed a higher activity for the joint control see RMS SYN , Fig. Moreover, the spatiotemporal organization of the muscle synergies differed between passive and assistive modality, except for S5 average R MAP-SYN over the 12 targets: 0.
In this work, we firstly extended our previous work [ 21 ] on the evaluation of ALEx analyzing joint kinematics and spinal maps and including statistical analysis for all movement directions, in order to deeper assess its transparency and its application as a rehabilitative tool. In this regard, we consider that the robot behaves transparently if movement execution with and without the robot show kinematically equivalent EE and joint trajectories and similar patterns of muscle activation and coordination.
Then, we were also interested in investigating the effects of different rehabilitative strategies and exercises yielded by the exoskeleton, in order to provide more insights on the use of robots for rehabilitation. In this regard, accordingly to previous findings on the effect of gravity compensation on upper limb muscle activity [ 31 , 41 ], we hypothesize that passive movements elicit a low but still coordinated muscle activity, analogously to what happens during fully supported upper limb movements.
Our results generally confirmed our hypothesis and, interestingly, we found that the choice of the desired trajectories for passive training may influence the inferred muscle organization. Muscle synergies obtained from the factorization of EMG signals collected during the performance of different motor tasks have been recently proposed by many authors to study muscle coordination and motor control [ 40 , 42 , 43 ].
Indeed, the combination of few muscle synergies can explain the main spatiotemporal characteristics of muscle activation during movements [ 44 , 45 ]. Moreover, the analysis of muscle synergies has also been proposed in rehabilitation to highlight the modifications of motor control due to several neural pathologies [ 46 ].
The muscle synergies obtained in our analysis generally agree with the literature that report from 2 to 8 muscle synergies during upper limb movements [ 40 , 44 , 45 , 47 — 49 ], but they offer a more compact description of the variability of the EMG signals respect than that already found by previous authors including similar muscle groups and adopting an equivalent motor task [ 31 , 50 ]. So far, spinal maps have been characterized mainly in lower limb motor tasks [ 22 — 25 ], and, for the best of our knowledge, no previous works have studied spinal maps during reaching movements.
We proposed this method since we believe that it represents a useful tool to explore muscle organization also for upper extremities, where the muscular timing activation is often more complex. The analysis of the MN activity offers the possibility to assess if different factors such as the use of the exoskeleton or the different modalities of control would have an impact on the spinal cord activity. In addition, in this work we proposed a combination of the two methodologies, i.
This approach has never been adopted so far, but it has been suggested by the spinal distribution of the Gaussian activation components estimating the timing activation of the muscle synergies, proposed by Ivanenko and colleagues [ 24 ]. Our results show that these three methodologies are meaningful to explore the information provided by the EMG signals and they were able to pinpoint higher differences across conditions than by looking at the features of the EMG envelops.
In fact, muscle synergies and their spatio-temporal organization proved to be sensitive to the biomechanical request of the task, i. While spinal maps were mainly sensitive to the variations of the level of the muscle activity across conditions, in particular between passive and assisted movements. Recordings were performed in three different sessions, in order to avoid muscle fatigue and adaptation to the device.
No significant differences were found in the performance of the movements across sessions, as shown by the EE trajectories and the joint angular excursions, and in the muscle activity, as shown by the preserved timing and level of activity. Only few differences, which could be ascribable to a slight variation in the electrode placement, were found for the level of activity on the first session for BICS and DMED. Overall, the repeatability of the kinematics and muscle activity across sessions may be favored by the easy and controlled setup of ALEx.
As previously showed [ 21 ], the movements performed with the exoskeleton were more accurate but slightly less smooth. The higher accuracy could be ascribable to a deeper attention caused by the unusual situation of executing movements wearing an exoskeleton, which could also enforce a higher number of movement corrections. Indeed, a decreased smoothness was also observed in reaching trajectories performed by healthy subjects using ABLE [ 51 ], a robotic device with a design similar to ALEx. Furthermore, the use of the exoskeleton resulted in modifications of the joint kinematics: the abduction-adduction and the rotation of the shoulder were reduced, while the flexion-extension of the shoulder and elbow augmented, in particular in some directions i.
Remarkably, an increased range for the elbow and the shoulder motion was reported also by using ABLE [ 51 ]. These modifications were reflected also in the muscle coordination. Indeed, the reaching task proposed in the experiment involved a significant modulation of the shoulder muscles for the gravity compensation and of the elbow flexors and extensors [ 21 , 31 ]. However, the use of ALEx induced a redistribution of the contribution of the muscle groups for the execution of the reaching task, as reflected by an enhanced activation of the most cervical segments of the spinal maps and by a reduction of the activity in the most thoracic segments.
Indeed, the contribution of the muscles involved in the control of the shoulder and of the elbow extension during free movements was substituted by a higher activation of the back muscles and of the elbow flexors. These evidences suggested a modification of the strategy adopted by the subjects while using the exoskeleton that could be due to postural adjustments or to the constrains provided by the structure of the exoskeleton.
Indeed, despite in both conditions we asked the subjects not to move their back, when wearing ALEx, the subjects were seating in a chair ensuring the posture of the back with seat belts, while for the free movements the back was unconstrained. Indeed, these DoFs are characterized by a shorter transmission and a consequent higher rigidity then the other joints. Moreover, a misalignment between the exoskeleton and the human limb, in particular at the level of the elbow joint, could cause the generation of undesirable interaction forces.
Active and passive movements are primary control paradigms adopted for robotic therapy [ 19 ]. Elucidating the differences between active and passive training may help understanding and improving robot-assisted therapy, since the knowledge about the promotion of motor learning and recovery by active and passive exercises is still poor [ 52 ]. Therefore, in this work we investigated the effects of passive movements i. Our results confirmed our preliminary hypothesis, and they showed that passive arm movements induce similar effects of fully supported ones [ 31 , 42 ]: in general, the passive training with ALEx elicited a significant muscle activity in most of the muscle groups, even though the activity was lower than during active reaching.
However, some differences were present in the spatio-temporal organization of muscle synergies in particular for S2 and S3 , proving that despite the preservation of muscle coordination and a similar overall spinal activity, the assistive modality would achieve a muscle output with a different organization of the spinal circuitries with respect to the free movements and the passive modality.
These differences seemed to be reduced when passive movements were elicited by trajectories previously recorded from the subjects. Therefore, desired trajectories for the passive training should be carefully evaluated in the robotic therapy.