Robotic Assistive Devices Research Articles

P-247 In vivo feasibility study of a robotic arm-assisted endoscopic submucosal dissection for early gastric cancer

Appropriate tissue tension and clear visibility of the dissection area using traction are essential for effective and safe endoscopic submucosal dissection (ESD). We developed a robotic assistive traction device for flexible endoscopy and compared its safety and efficiency in ESD between experienced and novice endoscopists. Robotic ESD was performed by experienced and novice endoscopist groups (n = 3, each). The outcomes included time to complete each ESD step, total procedure time, size of the dissected mucosa, rate of en bloc resection, and major adverse events. Furthermore, the incision and dissection speeds were compared between the groups. Twenty gastric lesions were resected from ten live pigs. In the experienced group, the mean incision speed was faster significantly. There was no significant difference in the submucosal dissection speed between the groups. No complications were reported in either group. In conclusion, ROSE can provide simple, effective, and safe multidirectional traction and countertraction during ESD. While performing robotic ESD, experienced endoscopists expressed satisfaction, and novices could perform ESD safely and efficiently. We expect our device to help trainees perform ESD in places where ESD education is difficult to obtain. The device must be made smaller, and its safety and efficacy in humans should be assessed in future studies.

Endoscopic submucosal dissection using a detachable assistant robot: a comparative in vivo feasibility study (with video).

Appropriate tissue tension and clear visibility of the dissection area using traction are essential for effective and safe endoscopic submucosal dissection (ESD). We developed a robotic assistive traction device for flexible endoscopy and compared its safety and efficiency in ESD between experienced and novice endoscopists. Robotic ESD was performed by experienced and novice endoscopist groups (n = 2, each). The outcomes included time to complete each ESD step, total procedure time, size of the dissected mucosa, rate of en bloc resection, and major adverse events. Furthermore, incision and dissection speeds were compared between groups. Sixteen gastric lesions were resected from nine live pigs. The submucosal incision speed was significantly faster in the expert group than in the novice group (P = 0.002). There was no significant difference in the submucosal dissection speed between the groups (P = 0.365). No complications were reported in either group. When the robot was assisting in the ESD procedure, the dissection speed improved significantly, especially in the novice surgeons. Our robotic device can provide simple, effective, and safe multidirectional traction during ESD.

An eye-tracking based robotic scrub nurse: proof of concept

BackgroundWithin surgery, assistive robotic devices (ARD) have reported improved patient outcomes. ARD can offer the surgical team a “third hand” to perform wider tasks and more degrees of motion in comparison with conventional laparoscopy. We test an eye-tracking based robotic scrub nurse (RSN) in a simulated operating room based on a novel real-time framework for theatre-wide 3D gaze localization in a mobile fashion.MethodsSurgeons performed segmental resection of pig colon and handsewn end-to-end anastomosis while wearing eye-tracking glasses (ETG) assisted by distributed RGB-D motion sensors. To select instruments, surgeons (ST) fixed their gaze on a screen, initiating the RSN to pick up and transfer the item. Comparison was made between the task with the assistance of a human scrub nurse (HSNt) versus the task with the assistance of robotic and human scrub nurse (R&HSNt). Task load (NASA-TLX), technology acceptance (Van der Laan’s), metric data on performance and team communication were measured.ResultsOverall, 10 ST participated. NASA-TLX feedback for ST on HSNt vs R&HSNt usage revealed no significant difference in mental, physical or temporal demands and no change in task performance. ST reported significantly higher frustration score with R&HSNt. Van der Laan’s scores showed positive usefulness and satisfaction scores in using the RSN. No significant difference in operating time was observed.ConclusionsWe report initial findings of our eye-tracking based RSN. This enables mobile, unrestricted hands-free human–robot interaction intra-operatively. Importantly, this platform is deemed non-inferior to HSNt and accepted by ST and HSN test users.

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Due to the compliance and continuous deformation capacity, rehabilitation devices driven by soft actuators are attached to widespread attention. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. Finally, several simulations and experiments are carried out to evaluate the performance of the developed system, and the results show that the developed system with the proposed controller can achieve expected control performance under various operations.

Brain-Computer Interface Coupled to a Robotic Hand Orthosis for Stroke Patients' Neurorehabilitation: A Crossover Feasibility Study.

Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients’ affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials.

Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds

Understanding the lower limb kinematic, kinetic, and electromyography (EMG) data interrelation in controlled speeds is challenging for fully assessing human locomotion conditions. This paper provides a complete dataset with the above-mentioned raw and processed data simultaneously recorded for sixteen healthy participants walking on a 10 meter-flat surface at seven controlled speeds (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h). The raw data include 3D joint trajectories of 24 retro-reflective markers, ground reaction forces (GRF), force plate moments, center of pressures, and EMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Biceps Femoris, and Vastus Lateralis. The processed data present gait cycle-normalized data including filtered EMG signals and their envelope, 3D GRF, joint angles, and torques. This study details the experimental setup and presents a brief validation of the data quality. The presented dataset may contribute to (i) validate and enhance human biomechanical gait models, and (ii) serve as a reference trajectory for personalized control of robotic assistive devices, aiming an adequate assistance level adjusted to the gait speed and user’s anthropometry.

Upper limbs’ assistive devices for stroke rehabilitation: a systematic review on design engineering solutions

This paper presents a systematic review focused on different mechanical design configurations (purely mechanical and mechatronic-assistive devices) used specifically to post-stroke rehabilitation and therapeutics activities. The process of synthesis of the 17 paper more relevant indicated the main tests prescribed and used for therapeutic and rehabilitation activities poststroke. During the review process, it was observed that there is no clear to choice of design configuration assistive device in a multidiscipline domain, specifically for upper limbs. There was no identification of papers that presented a comparative study of performance in rehabilitation treatment using mechanical and mechatronic robotic devices. The systematic review indicated that there are no significant differences between DT and CT, showing similar performances in rehabilitation therapies. The Fugl–Meyer assessment was the main outcome measure used in the rehabilitation treatments. All outcome measures presented in the studies were analyzed individually. The synthesis process presented in this paper indicated similar results between purely mechanical and mechatronic robotic assistive devices on the performance measures during the rehabilitation process in post-stroke patients. The similarity between performance of the purely mechanical and mechatronic-assistive devices for rehabilitation and therapeutic treatments reinforces the importance of the design for assistive technology in order to focus on ease of use, security, reliability, ergonomics and possibility of home use.

Natural Walking With Musculoskeletal Models Using Deep Reinforcement Learning

Human gait optimality has been investigated recently, with the development of detailed musculoskeletal models, through trajectory optimization approaches or deep reinforcement learning (DRL). Trajectory optimization studies are limited by the trajectory length and can only generate open-loop solutions. While existing DRL solutions provide closed-loop control policies without trajectory length limit, they either do not evaluate the naturalness of the behaviour, or directly impose experimental tracking data. In this letter, a DRL-based approach is proposed with a nature-inspired curriculum learning (CL) scheme and a neuromechanically-inspired reward function. This approach generates close-to-natural human walking without the aid of experimental data. Our CL scheme is realized by an evolving reward function, targeting simpler behaviours such as standing and stepping first, then gradually refining the gait. The emerged gait from the closed-loop stochastic policy demonstrated a strong correlation with human gait kinematics, with Pearson correlations of 0.95 and 0.83 at the hip and knee, respectively, and higher gait symmetry than two other DRL-based control policies without CL. Our approach was also found to have efficient convergence to walking-capable policy. This approach can facilitate the development of assistive robotic systems by providing a “human” controller, and could enable decentralized adaptation between the agent and the assistive robotic devices.

A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

Robotic assisted CyberKnife radiosurgery for the treatment of choroidal metastasis.

Choroidal metastases occur in many patients with systemic cancer and limit quality of life due to visual deterioration or pain. The limited prognosis of these patients demand treatment approaches that aim at a quick response and easy application. We evaluated a robotic assisted radiosurgery device, the CyberKnife, in the treatment of choroidal metastasis. Following retrobulbar anaesthesia, target volume was defined by an interdisciplinary team using gadolinium-contrast-enhanced MRI, computer tomography (CT) as well as all previously obtained clinical data. Radiation was delivered in a single fraction with a CyberKnife at a radiation dose of 21 Gy with a 70% isodose. We evaluated tumour height, reflectivity, visual acuity and side effects over the course of the follow-up. A total of four patients were included in this report. Mean follow-up time was 31 months, and all patients showed decreased tumour volume and signs of fibrosis during follow-up. One patient remained stable in terms of visual acuity, two patients lost visual acuity and one patient improved and had a functional visual acuity at last follow-up. One patient developed radiation retinopathy with subsequent neovascular glaucoma that needed further management. All other patients did not show any signs of radiation induced side effects. We report on the outcome of robotic CyberKnife radiosurgery in the treatment of uveal metastasis. We believe that single session, outpatient treatment delivery that can be facilitated with CyberKnife radiosurgery offer some benefits for this particularly sick group of patients.

Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

Objective. Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal. Approach. This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis. Main results. The optimum window size ranges are from 200–300 ms for the disjoint and 225–300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%–30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%–30% (p-values < 0.05). Significance. The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.

Continuous Gait Phase Estimation Using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds.

User gait phase estimation plays a key role for the seamless control of the lower-limb robotic assistive devices (e.g., exoskeletons or prostheses) during ambulation. To achieve this, several studies have attempted to estimate the gait phase using a thigh or shank angle. However, their estimation resulted in some deviation from the actual walking and varied across the walking speeds. In this study, we investigated the different setups using for the machine learning approach to obtain more accurate and consistent gait phase estimation for the robotic transfemoral prosthesis over different walking speeds. Considering the transfemoral prosthetic application, we proposed two different sensor setups: i) the angular positions and velocities of both thigh and torso (S1) and ii) the angular positions and velocities of both thigh and torso, and heel force data (S2). The proposed setups and method are experimentally evaluated with three healthy young subjects at four different walking speeds: 0.5, 1.0, 1.5, and 2.0 m/s. Both results showed robust and accurate gait phase estimation with respect to the ground truth (loss value of S1: 4.54e-03 Vs. S2: 4.70e-03). S1 had the advantage of a simple equipment setup using only two IMUs, while S2 had the advantage of estimating more accurate heel-strikes than S1 by using additional heel force data. The choice between the two sensor setups can depend on the researchers' preference in consideration of the device setup or the focus of the interest.

Prototype of natural user interface applied to a robotic arm for medical attention preventing nosocomial infections in healthcare personnel

In this work, an experimental prototype of natural user interface based on Kinect for the tele-operation of a robotic arm, was developed as a technological support to contribute in facing the global crisis of health generated by COVID-19, a dangerous and highly transmissible virus. Until now, it is causing thousands of deaths and growing of contagion rates around the world, involving a serious situation for healthcare personnel that works in hospitals attending infected population. This system was proposed with the aim to control a robotic arm for medical purposes to achieve a quality medical care to COVID-19 patients without risk implications for healthcare workers associated to nosocomial infections due to direct contact with infected patients, contaminated medical equipment, surgical objects and surfaces. The developed prototype is able of being manipulated in real time requiring neither physical controller nor any contact device to carry out its functions, but simply motion or gesture from the user’s arm. It can be applied in areas such as teleoperation, tele-rehabilitation, telehealth nursing, assistive and therapeutic robotic devices, elderly care which are the last tendency in medicine at this time. Kinect V2, the Software Development kit SDK 2.0, Microsoft visual C# and Arduino were used for this purpose.

Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach.

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control.

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

SEMG signal classification using KNN classifier with FD and TFD features

For developing thebio-engineering inspired human–machine interface, surface electromyography (SEMG) signals have been widely exploited in the last decade. Movement identification of SEMG signal is considered the main cause of success in the area of assistive robotic devices like an exoskeleton. In this study, I compared the frequency domain (FD) and time–frequency domain (TFD) features by using k-nearest neighbor (KNN) classifier for identification of six different hand movements. Discrete Wavelet Transform (DWT) is utilized mainly for de-noising as well as TFD feature extraction of the sEMG signal before the feature extraction. Finally, two feature vector is formed which consists of all FD and TFD features for classification purpose. KNNyielded95.5% accuracy with TFD feature vector whereas classification accuracy with FD feature vector was found 89%. The result exhibits that TFD features have a greater capability for sEMG signal movements identificationin comparison withFD features. Therefore TFD features were suggested instead of FD featuresfor designing the robotic suit for assistive technology..

Hb vsEMG signal classification with time domain and Frequency domain features using LDA and ANN classifier

The surface electromyography (sEMG) signals have been widely employed for the development of the human–machine interface and have enormous bio-engineering applications. Movement identification of SEMG signal plays a significant factor in the designing of human assistive robotic devices. This work compared the time domain (TD) and frequency domain (FD) features by using linear discriminant analysis (LDA) and artificial neural network (ANN) classifiers for six different hand movements’ identification. Discrete Wavelet Transform is employed mainly for de-noising the sEMG signal before the feature extraction. Finally, a feature vector is formed which consists of all TD and FD features for classification purpose. ANN exhibited96.4% accuracy and found better as compared to the LDA classifier whereas classification accuracy of LDA classifier was found 94.5%. The resultexhibits that ANN has a greater ability for sEMG signal classification as compared to LDA classifier and further suggested to design the assistive robotic technology.

Interim safety analysis of the first-in-human clinical trial of the Versius surgical system, a new robot-assisted device for use in minimal access surgery

ObjectiveThe aim of this study was to provide an interim safety analysis of the first 30 surgical procedures performed using the Versius Surgical System.BackgroundRobot-assisted laparoscopy has been developed to overcome some of the important limitations of conventional laparoscopy. The new system is currently undergoing a first-in-human prospective clinical trial to confirm the safety and effectiveness of the device when performing minimal access surgery (MAS).MethodsProcedures were performed using Versius by a lead surgeon supported by an operating room (OR) team. Male or female patients aged between 18 and 65 years old and requiring elective minor or intermediate gynaecological or general surgical procedures were enrolled. The primary endpoint was the rate of unplanned conversion of procedures to other MAS or open surgery.ResultsThe procedures included nine cholecystectomies, six robot-assisted total laparoscopic hysterectomies, four appendectomies, five diagnostic laparoscopy cases, two oophorectomies, two fallopian tube recanalisation procedures, an ovarian cystectomy and a salpingo-oophorectomy procedure. All procedures were completed successfully without the need for conversion to MAS or open surgery. No patient returned to the OR within 24 h of surgery and readmittance rate at 30 and 90 days post-surgery was 1/30 (3.3%) and 2/30 (6.7%), respectively.ConclusionsThis first-in-human interim safety analysis demonstrates that the Versius Surgical System is safe and can be used to successfully perform minor or intermediate gynaecological and general surgery procedures. The cases presented here provide evidence that the Versius clinical trial can continue to extend recruitment and begin to include major procedures, in alignment with the IDEAL-D Framework Stage 2b: Exploration.

Usability and task load comparison between a robotic assisted transfer device and a mechanical floor lift during caregiver assisted transfers on a care recipient

Introduction The RATD represents a novel methodology to reduce strain, manoeuvring, and cognitive load a caregiver experiences when conducting transfers on a mannequin. However, caregivers who used this new technology report suggested adjustments regarding the robot’s human machine interface and shape as to improve transfer efficiency and comfort for care recipients. The purpose of this study was to test a redesigned RATD and compare its ergonomics during a transfer to those of a mechanical floor lift. Methods This was cross sectional protocol. As opposed to prior research which used a mannequin, caregivers in this study (N = 28) partnered with, and transferred, a mobility device user (N = 28) at three unique surfaces. Information about task demand and usability was collected from surveys after use of each device at each surface. Results Results indicated reduced physical demand (p = .004) and discomfort frequency (p = .01) in caregivers conducting the transfers with the RATD compared to the mechanical floor lift. Care recipients reported no significant differences between both transfer devices. Critiques with the interface, the harness and sling, and the robot’s rigidity indicated more work is needed before introducing this technology to a larger market. Conclusions: The RATD represents a promising new intervention for transferring and handling care recipients who use wheelchairs. However, while caregivers report reduced physical demand and discomfort, more work is required to advance the ease of the human machine interface, the amount of space allowed for the robot to operate, and the ability of the care recipient to operate the technology independently. IMPLICATIONS FOR REHABILITATION Caregivers report significant physical and mental stress while transferring clients in and out of a wheelchair. Clinical standard transfer equipment is limited in the space which it can be used. Robots, particularly those portable and powered, have the ability to not only make the transfer experience safer, but also expand the applications this equipment can provide.

Technology acceptance and perceptions of robotic assistive devices by older adults – implications for exoskeleton design

Aim This study explored and interpreted insights expressed by a cohort of older adults related to their life experience, their experiences using or assisting someone with assistive devices, and their perceptions of robots and robotic assistive devices, including lower limb exoskeletons. Method A grounded theory study was undertaken with 24 older adult participants over five months. Each participant participated in a structured interviewed regarding their experiences with technologies and in particular their perceptions of assistive technologies. Themes from the interviews were coded using Nvivo software. Results Five main themes emerged from this study – (1) Aging & life stage experiences, (2) Quality of Life, (3) Assistive Technologies, (4) Health Conditions & Care, (5) Products & Service Systems. These have influenced new constructs for a hybrid design tool that incorporates stages of Usability and TAMs (Technology Acceptance Models) to gauge (a) Perception, (b) Experience and (c) Perceived Impact by older adults of lower limb exoskeletons. Conclusions: Emerging technologies such as robotic assistive devices require a specific enquiry to understand how best to optimise acceptance by older adults and avoid feelings by them of frustration, embarrassment and ultimately abandonment of these devices. Implications for rehabilitation Older adults frequently require rehabilitation and assistance with ambulation Exoskeletons are forms of assistive technologies for rehabilitation, and they are moving from clinical use to more day care use, including as part of daily living These results help explain factors related to the perception of exoskeletons by older adults, which if considered during exoskeleton design, could improve the technology uptake and compliance with this technology use by these users