Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson's disease: a case series.

INTRODUCTION: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice. METHODS: In this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05). RESULTS: After training subjects improved gait speed (Pre-trn_No-Bf: 0.72(0.59,0.72) m/sec; Post-trn_Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn_No-Bf: 0.87(0.81,0.96) meters; Post-trn_Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects' walking distance improved (Pre-trn_No-Bf: 97.5 (80.3,110.8) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn_No-Bf: 29.7(26.8,31.7) %; Post-trn_Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn_No-Bf: 108(103.8,116.7) step/min; Pre-trn_Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn_No-Bf: 112.5(97.5,124.5) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events. CONCLUSION: The BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments. TRIAL REGISTRATION: The study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy).

Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).

BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. METHODS: Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.

Uneven Terrain Recognition Using Neuromorphic Haptic Feedback.

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot-ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb.

A Multimodal Sensory Apparatus for Robotic Prosthetic Feet Combining Optoelectronic Pressure Transducers and IMU.

Timely and reliable identification of control phases is functional to the control of a powered robotic lower-limb prosthesis. This study presents a commercial energy-store-and-release foot prosthesis instrumented with a multimodal sensory system comprising optoelectronic pressure sensors (PS) and IMU. The performance was verified with eight healthy participants, comparing signals processed by two different algorithms, based on PS and IMU, respectively, for real-time detection of heel strike (HS) and toe-off (TO) events and an estimate of relevant biomechanical variables such as vertical ground reaction force (vGRF) and center of pressure along the sagittal axis (CoPy). The performance of both algorithms was benchmarked against a force platform and a marker-based stereophotogrammetric motion capture system. HS and TO were estimated with a time error lower than 0.100 s for both the algorithms, sufficient for the control of a lower-limb robotic prosthesis. Finally, the CoPy computed from the PS showed a Pearson correlation coefficient of 0.97 (0.02) with the same variable computed through the force platform.

Robot-mediated overground gait training for transfemoral amputees with a powered bilateral hip orthosis: a pilot study.

BACKGROUND: Transfemoral amputation is a serious intervention that alters the locomotion pattern, leading to secondary disorders and reduced quality of life. The outcomes of current gait rehabilitation for TFAs seem to be highly dependent on factors such as the duration and intensity of the treatment and the age or etiology of the patient. Although the use of robotic assistance for prosthetic gait rehabilitation has been limited, robotic technologies have demonstrated positive rehabilitative effects for other mobility disorders and may thus offer a promising solution for the restoration of healthy gait in TFAs. This study therefore explored the feasibility of using a bilateral powered hip orthosis (APO) to train the gait of community-ambulating TFAs and the effects on their walking abilities. METHODS: Seven participants (46-71 years old with different mobility levels) were included in the study and assigned to one of two groups (namely Symmetry and Speed groups) according to their prosthesis type, mobility level, and prior experience with the exoskeleton. Each participant engaged in a maximum of 12 sessions, divided into one Enrollment session, one Tuning session, two Assessment sessions (conducted before and after the training program), and eight Training sessions, each consisting of 20 minutes of robotically assisted overground walking combined with additional tasks. The two groups were assisted by different torque-phase profiles, aiming at improving symmetry for the Symmetry group and at maximizing the net power transferred by the APO for the Speed group. During the Assessment sessions, participants performed two 6-min walking tests (6mWTs), one with (Exo) and one without (NoExo) the exoskeleton, at either maximal (Symmetry group) or self-selected (Speed group) speed. Spatio-temporal gait parameters were recorded by commercial measurement equipment as well as by the APO sensors, and metabolic efficiency was estimated via the Cost of Transport (CoT). Additionally, kinetic and kinematic data were recorded before and after treatment in the NoExo condition. RESULTS: The one-month training protocol was found to be a feasible strategy to train TFAs, as all participants smoothly completed the clinical protocol with no relevant mechanical failures of the APO. The walking performance of participants improved after the training. During the 6mWT in NoExo, participants in the Symmetry and Speed groups respectively walked 17.4% and 11.7% farther and increased walking speed by 13.7% and 17.9%, with improved temporal and spatial symmetry for the former group and decreased energetic expenditure for the latter. Gait analysis showed that ankle power, step width, and hip kinematics were modified towards healthy reference levels in both groups. In the Exo condition metabolic efficiency was reduced by 3% for the Symmetry group and more than 20% for the Speed group. CONCLUSIONS: This study presents the first pilot study to apply a wearable robotic orthosis (APO) to assist TFAs in an overground gait rehabilitation program. The proposed APO-assisted training program was demonstrated as a feasible strategy to train TFAs in a rehabilitation setting. Subjects improved their walking abilities, although further studies are required to evaluate the effectiveness of the APO compared to other gait interventions. Future protocols will include a lighter version of the APO along with optimized assistive strategies.

Survey of transfemoral amputee experience and priorities for the user-centered design of powered robotic transfemoral prostheses.

BACKGROUND: Transfemoral amputees experience a complex host of physical, psychological, and social challenges, compounded by the functional limitations of current transfemoral prostheses. However, the specific relationships between human factors and prosthesis design and performance characteristics have not yet been adequately investigated. The present study aims to address this knowledge gap. METHODS: A comprehensive single-cohort survey of 114 unilateral transfemoral amputees addressed a broad range of demographic and clinical characteristics, functional autonomy, satisfaction and attitudes towards their current prostheses, and design priorities for an ideal transfemoral prosthesis, including the possibility of active assistance from a robotic knee unit. The survey was custom-developed based on several standard questionnaires used to assess motor abilities and autonomy in activities of daily living, prosthesis satisfaction, and quality of life in lower-limb amputees. Survey data were analyzed to compare the experience (including autonomy and satisfaction) and design priorities of users of transfemoral prostheses with versus without microprocessor-controlled knee units (MPKs and NMPKs, respectively), with a subsequent analyses of cross-category correlation, principal component analysis (PCA), cost-sensitivity segmentation, and unsupervised K-means clustering applied within the most cost-sensitive participants, to identify functional groupings of users with respect to their design priorities. RESULTS: The cohort featured predominantly younger (< 50 years) traumatic male amputees with respect to the general transfemoral amputee population, with pronounced differences in age distribution and amputation etiology (traumatic vs. non-traumatic) between MPK and NMPK groups. These differences were further reflected in user experience, with MPK users reporting significantly greater overall functional autonomy, satisfaction, and sense of prosthesis ownership than those with NMPKs, in conjunction with a decreased incidence of instability and falls. Across all participants, the leading functional priorities for an ideal transfemoral prosthesis were overall stability, adaptability to variable walking velocity, and lifestyle-related functionality, while the highest-prioritized general characteristics were reliability, comfort, and weight, with highly variable prioritization of cost according to reimbursement status. PCA and user clustering analyses revealed the possibility for functionally relevant groupings of prosthesis features and users, based on their differential prioritization of these features-with implications towards prosthesis design tradeoffs. CONCLUSIONS: This study's findings support the understanding that when appropriately prescribed according to patient characteristics and needs in the context of a proactive rehabilitation program, advanced transfemoral prostheses promote patient mobility, autonomy, and overall health. Survey data indicate overall stability, modularity, and versatility as key design priorities for the continued development of transfemoral prosthesis technology. Finally, observed associations between prosthesis type, user experience, and attitudes concerning prosthesis ownership suggest both that prosthesis characteristics influence device acceptance and functional outcomes, and that psychosocial factors should be specifically and proactively addressed during the rehabilitation process.

Pressure-Sensitive Insoles for Real-Time Gait-Related Applications.

Wearable robotic devices require sensors and algorithms that can recognize the user state in real-time, in order to provide synergistic action with the body. For devices intended for locomotion-related applications, shoe-embedded sensors are a common and convenient choice, potentially advantageous for performing gait assessment in real-world environments. In this work, we present the development of a pair of pressure-sensitive insoles based on optoelectronic sensors for the real-time estimation of temporal gait parameters. The new design makes use of a simplified sensor configuration that preserves the time accuracy of gait event detection relative to previous prototypes. The system has been assessed relatively to a commercial force plate recording the vertical component of the ground reaction force (vGRF) and the coordinate of the center of pressure along the so-called progression or antero-posterior plane (CoPAP) in ten healthy participants during ground-level walking at two speeds. The insoles showed overall median absolute errors (MAE) of 0.06 (0.02) s and 0.04 (0.02) s for heel-strike and toe-off recognition, respectively. Moreover, they enabled reasonably accurate estimations of the stance phase duration (2.02 (2.03) % error) and CoPAP profiles (Pearson correlation coefficient with force platform ρCoP = 0.96 (0.02)), whereas the correlation with vGRF measured by the force plate was lower than that obtained with the previous prototype (ρvGRF = 0.47 (0.20)). These results confirm the suitability of the insoles for online sensing purposes such as timely gait phase estimation and discrete event recognition.

Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks.

BACKGROUND: To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user's movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user's motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities. METHODS: Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Go-backward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method. RESULTS: The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications. CONCLUSIONS: The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.

Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton.

The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users' movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class "no lift") were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement.

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton.

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user's physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject's workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

Physical human-robot interaction of an active pelvis orthosis: toward ergonomic assessment of wearable robots.

BACKGROUND: In human-centered robotics, exoskeletons are becoming relevant for addressing needs in the healthcare and industrial domains. Owing to their close interaction with the user, the safety and ergonomics of these systems are critical design features that require systematic evaluation methodologies. Proper transfer of mechanical power requires optimal tuning of the kinematic coupling between the robotic and anatomical joint rotation axes. We present the methods and results of an experimental evaluation of the physical interaction with an active pelvis orthosis (APO). This device was designed to effectively assist in hip flexion-extension during locomotion with a minimum impact on the physiological human kinematics, owing to a set of passive degrees of freedom for self-alignment of the human and robotic hip flexion-extension axes. METHODS: Five healthy volunteers walked on a treadmill at different speeds without and with the APO under different levels of assistance. The user-APO physical interaction was evaluated in terms of: (i) the deviation of human lower-limb joint kinematics when wearing the APO with respect to the physiological behavior (i.e., without the APO); (ii) relative displacements between the APO orthotic shells and the corresponding body segments; and (iii) the discrepancy between the kinematics of the APO and the wearer's hip joints. RESULTS: The results show: (i) negligible interference of the APO in human kinematics under all the experimented conditions; (ii) small (i.e., < 1 cm) relative displacements between the APO cuffs and the corresponding body segments (called stability); and (iii) significant increment in the human-robot kinematics discrepancy at the hip flexion-extension joint associated with speed and assistance level increase. CONCLUSIONS: APO mechanics and actuation have negligible interference in human locomotion. Human kinematics was not affected by the APO under all tested conditions. In addition, under all tested conditions, there was no relevant relative displacement between the orthotic cuffs and the corresponding anatomical segments. Hence, the physical human-robot coupling is reliable. These facts prove that the adopted mechanical design of passive degrees of freedom allows an effective human-robot kinematic coupling. We believe that this analysis may be useful for the definition of evaluation metrics for the ergonomics assessment of wearable robots.

Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation.

Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master-slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator's hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers' hands movements.

The rubber foot illusion.

BACKGROUND: Lower-limb amputation causes the individual a huge functional impairment due to the lack of adequate sensory perception from the missing limb. The development of an augmenting sensory feedback device able to restore some of the missing information from the amputated limb may improve embodiment, control and acceptability of the prosthesis. FINDINGS: In this work we transferred the Rubber Hand Illusion paradigm to the lower limb. We investigated the possibility of promoting body ownership of a fake foot, in a series of experiments fashioned after the RHI using matched or mismatched (vibrotactile) stimulation. The results, collected from 19 healthy subjects, demonstrated that it is possible to elicit the perception of possessing a rubber foot when modality-matched stimulations are provided synchronously on the biological foot and to the corresponding rubber foot areas. Results also proved that it is possible to enhance the illusion even with modality-mismatched stimulation, even though illusion was lower than in case of modality-matched stimulation. CONCLUSIONS: We demonstrated the possibility of promoting a Rubber Foot Illusion with both matched and mismatched stimulation.

Analysis of relative displacement between the HX wearable robotic exoskeleton and the user's hand.

BACKGROUND: Advances in technology are allowing for the production of several viable wearable robotic devices to assist with activities of daily living and with rehabilitation. One of the most pressing limitations to user satisfaction is the lack of consistency in motion between the user and the robotic device. The displacement between the robot and the body segment may not correspond because of differences in skin and tissue compliance, mechanical backlash, and/or incorrect fit. FINDINGS: This report presents the results of an analysis of relative displacement between the user's hand and a wearable exoskeleton, the HX. HX has been designed to maximize comfort, wearability and user safety, exploiting chains with multiple degrees-of-freedom with a modular architecture. These appealing features may introduce several uncertainties in the kinematic performances, especially when considering the anthropometry, morphology and degree of mobility of the human hand. The small relative displacements between the hand and the exoskeleton were measured with a video-based motion capture system, while the user executed several different grips in different exoskeleton modes. CONCLUSIONS: The analysis furnished quantitative results about the device performance, differentiated among device modules and test conditions. In general, the global relative displacement for the distal part of the device was in the range 0.5-1.5 mm, while within 3 mm (worse but still acceptable) for displacements nearest to the hand dorsum. Conclusions over the HX design principles have been drawn, as well as guidelines for future developments.

Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG).

BACKGROUND: Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions. FINDINGS: 12 healthy volunteers (8 male, mean age 28.1 ± 3.63y) used EEG (condition #1) and hybrid EEG/EOG (condition #2) signals to control a hand exoskeleton. Motor imagery-related brain activity was translated into exoskeleton-driven hand closing motions. Unintended motions could be interrupted by eye movement-related EOG signals. In order to evaluate BNCI control and safety, participants were instructed to follow a visual cue indicating either to move or not to move the hand exoskeleton in a random order. Movements exceeding 25% of a full grasping motion when the device was not supposed to be moved were defined as safety violation. While participants reached comparable control under both conditions, safety was frequently violated under condition #1 (EEG), but not under condition #2 (EEG/EOG). CONCLUSION: EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.

Effects of lower limb length and body proportions on the energy cost of overground walking in older persons.

BACKGROUND: Although walking has been extensively investigated in its biomechanical and physiological aspects, little is known on whether lower limb length and body proportions affect the energy cost of overground walking in older persons. METHODS: We enrolled 50 men and 12 women aged 65 years and over, mean 69.1 ± SD 5.4, who at the end of their cardiac rehabilitation program performed the six-minute walk test while wearing a portable device for direct calorimetry and who walked a distance comparable to that of nondisabled community-dwelling older persons. RESULTS: In the multivariable regression model (F = 12.75, P < 0.001, adjusted R(2) = 0.278) the energy cost of overground walking, expressed as the net energy expenditure, in kg(-1) sec(-1), needed to provide own body mass with 1 joule kinetic energy, was inversely related to lower limb length and directly related to lower limb length to height ratio (ß ± SE(ß) = -3.72 × 10(-3) ± 0.74 × 10(-3), P < 0.001, and 6.61 × 10(-3) ± 2.14 × 10(-3), P = 0.003, resp.). Ancillary analyses also showed that, altogether, 1 cm increase in lower limb length reduced the energy cost of overground walking by 2.57% (95%CI 2.35-2.79). CONCLUSIONS: Lower limb length and body proportions actually affect the energy cost of overground walking in older persons.

A wireless flexible sensorized insole for gait analysis.

This paper introduces the design and development of a novel pressure-sensitive foot insole for real-time monitoring of plantar pressure distribution during walking. The device consists of a flexible insole with 64 pressure-sensitive elements and an integrated electronic board for high-frequency data acquisition, pre-filtering, and wireless transmission to a remote data computing/storing unit. The pressure-sensitive technology is based on an optoelectronic technology developed at Scuola Superiore Sant'Anna. The insole is a low-cost and low-power battery-powered device. The design and development of the device is presented along with its experimental characterization and validation with healthy subjects performing a task of walking at different speeds, and benchmarked against an instrumented force platform.

Online phase detection using wearable sensors for walking with a robotic prosthesis.

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training.

Evaluation of antigravitational support levels provided by a passive upper-limb occupational exoskeleton in repetitive arm movements.

Upper-limb occupational exoskeletons to support the workers' upper arms are typically designed to provide antigravitational support. Although typical work activities require workers to perform static and dynamic actions, the majority of the studies in literature investigated the effects of upper-limb occupational exoskeletons in static and quasi-static activities, while only a few works focused on dynamic tasks. This article presents a systematic evaluation of the effects of different levels of antigravitational support (from about 60% to 100% of the arm gravitational load) provided by a passive upper-limb occupational exoskeleton on muscles' activity during repetitive arm movements. The effect of the exoskeleton on muscle activity was evaluated by the comparison of muscle activations with and without the exoskeleton. The average muscle activation was computed considering shoulder full flexion-extension cycles, and sub-movements, namely the arm-lifting (i.e., flexion) and arm-lowering (i.e., extension) movements. Results showed a quasi-linear correlation between antigravitational support and muscle activity reductions, both when considering the full flexion-extension cycle and in the arm-lifting movement (reductions were up to 64 and 61% compared to not wearing the exoskeleton, respectively). When considering the arm-lowering movement, providing antigravitational support close to or higher than 100% of the arm gravitational load led to increased muscle activations of the extensors (up to 127%), suggesting that such an amount of antigravitational support may be not effective for a complete biomechanical load reduction on the shoulder district in dynamic tasks.

Assessment of Sensorized Insoles in Balance and Gait in Individuals With Parkinson's Disease.

Individuals with Parkinson's disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects' activities. In this study, we verified the capability of a pair of sensorized shoes (including pressure-sensitive insoles and one inertial measurement unit) in assessing ground-level walking and body weight shift exercises. The shoes can potentially be combined with a sensory biofeedback module that provides vibrotactile cues to individuals. Sensorized shoes have been assessed in terms of the capability of detecting relevant gait events (heel strike, flat foot, toe off), estimating spatiotemporal parameters of gait (stance, swing, and double support duration, stride length), estimating gait variables (vertical ground-reaction force, vGRF; coordinate of the center of pressure along the longitudinal axes of the feet, yCoP; and the dorsiflexion angle of the feet, Pitch angle). The assessment compared the outcomes with those extracted from the gold standard equipment, namely force platforms and a motion capture system. Results of this comparison with 9 PD subjects showed an overall median absolute error lower than 0.03 s in detecting the foot-contact, foot-off, and heel-off gait events while performing ground-level walking and lower than 0.15 s in body weight shift exercises. The computation of spatiotemporal parameters of gait showed median errors of 1.62 % of the stance phase duration and 0.002 m of the step length. Regarding the estimation of vGRF, yCoP, and Pitch angle, the median across-subjects Pearson correlation coefficient was 0.90, 0.94, and 0.91, respectively. These results confirm the suitability of the sensorized shoes for quantifying biomechanical features during body weight shift and gait exercises of PD and pave the way to exploit the biofeedback modules of the bidirectional interface in future studies.