Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User's Metabolic and Muscular Effort.

Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton's assistance in real time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users' physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user's metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (n = 5), making faster (10 s) and less noisy estimations than a respirometer (K5, Cosmed). The HITL reduced the user's metabolic cost by 7.3% and 5.9% compared to the zero-torque and no-device conditions, respectively, and reduced the interaction torque by 32.3% compared to a zero-torque control (n = 1). The developed HITL control surpassed a non-exoskeleton and zero-torque condition regarding the user's physical effort, even for a task such as slow walking. Furthermore, the user-specific control had a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential of HITL controls in assisted walking.

Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.

OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans. METHODS: A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices. RESULTS: The developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework. CONCLUSIONS: This study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice. CLINICAL RELEVANCE STATEMENT: The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment. KEY POINTS: • Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.

Knowledge distillation on individual vertebrae segmentation exploiting 3D U-Net.

Recent advances in medical imaging have highlighted the critical development of algorithms for individual vertebral segmentation on computed tomography (CT) scans. Essential for diagnostic accuracy and treatment planning in orthopaedics, neurosurgery and oncology, these algorithms face challenges in clinical implementation, including integration into healthcare systems. Consequently, our focus lies in exploring the application of knowledge distillation (KD) methods to train shallower networks capable of efficiently segmenting vertebrae in CT scans. This approach aims to reduce segmentation time, enhance suitability for emergency cases, and optimize computational and memory resource efficiency. Building upon prior research in the field, a two-step segmentation approach was employed. Firstly, the spine's location was determined by predicting a heatmap, indicating the probability of each voxel belonging to the spine. Subsequently, an iterative segmentation of vertebrae was performed from the top to the bottom of the CT volume over the located spine, using a memory instance to record the already segmented vertebrae. KD methods were implemented by training a teacher network with performance similar to that found in the literature, and this knowledge was distilled to a shallower network (student). Two KD methods were applied: (1) using the soft outputs of both networks and (2) matching logits. Two publicly available datasets, comprising 319 CT scans from 300 patients and a total of 611 cervical, 2387 thoracic, and 1507 lumbar vertebrae, were used. To ensure dataset balance and robustness, effective data augmentation methods were applied, including cleaning the memory instance to replicate the first vertebra segmentation. The teacher network achieved an average Dice similarity coefficient (DSC) of 88.22% and a Hausdorff distance (HD) of 7.71 mm, showcasing performance similar to other approaches in the literature. Through knowledge distillation from the teacher network, the student network's performance improved, with an average DSC increasing from 75.78% to 84.70% and an HD decreasing from 15.17 mm to 8.08 mm. Compared to other methods, our teacher network exhibited up to 99.09% fewer parameters, 90.02% faster inference time, 88.46% shorter total segmentation time, and 89.36% less associated carbon (CO2) emission rate. Regarding our student network, it featured 75.00% fewer parameters than our teacher, resulting in a 36.15% reduction in inference time, a 33.33% decrease in total segmentation time, and a 42.96% reduction in CO2 emissions. This study marks the first exploration of applying KD to the problem of individual vertebrae segmentation in CT, demonstrating the feasibility of achieving comparable performance to existing methods using smaller neural networks.

Digital biomarkers of mobility and quality of life in Parkinson's disease based on a wearable motion analysis LAB.

BACKGROUND AND OBJECTIVE: Functional mobility, an indicator of the quality of life (QoL), requires fast and flexible changes during motion, which are limited in Parkinson's disease (PD). Recent body-worn sensors have emerged in the last decades as potential solutions to produce digital biomarkers able to quantify mobility outside routine consultations and during real-life scenarios for multiple days at a time. The proposed research aims to study the ability of a wearable motion analysis lab, developed by our team, to produce digital biomarkers of mobility and QoL levels in patients with PD. METHODS: A cross-sectional study was followed, including 40 patients stratified into three subgroups according to a clinic motor examination and a QoL questionnaire. RESULTS: The achieved outcomes demonstrate the ability of the proposed high-tech solution to measure prototypical gait impairments and discriminate motor condition (AUC=0,890) and patients' QoL levels (AUC=0,950). Also, from the measured multiple gait-associated parameters, we identified the variables with the most potential to be applied as digital biomarkers of mobility (67 % of the metrics) and QoL (72 % of the metrics) in PD. CONCLUSIONS: Overall, we confirmed our hypothesis of using our body-worn sensor-based solution for passive or active monitoring of mobility and QoL in PD to produce objective, feasible, and continuous digital biomarkers.

Internet of medical things. Measurement of respiratory dynamics using wearable sensors in post-COVID-19 patients.

Nowadays, the measurement of respiratory dynamics is underrated at clinical setting and in the daily life of a subject and it still represents a challenge from a technical and medical point of view. In this article we propose a concept to measure some of its parameters, such as the respiratory rate (RR), using four inertial sensors. Two different experiments were performed to validate the concept. We analyzed the most suitable placement of each sensor to assess those features and we studied the reliability of the system to measure abnormal parameters of respiration (tachypnea, bradypnea and breath holding). Finally, we measured post-COVID-19 patients, some of them with breath alterations after more than a year of the diagnosis. Experimental results showed that the proposed system could be potentially used to measure the respiratory dynamics at clinical setting. Moreover, while RR can be easily calculated by any sensor, other parameters need to be measured with a sensor in a particular position.

Hoy en día, la medición de la dinámica respiratoria está infravalorada en el ámbito clínico y en la vida diaria de un sujeto y sigue representando un reto desde el punto de vista técnico y médico. En este artículo proponemos un concepto para medir algunos de sus parámetros, como la frecuencia respiratoria (FR), utilizando cuatro sensores inerciales. Se realizaron dos experimentos diferentes para validar el concepto. Analizamos la colocación más adecuada de cada sensor para evaluar esas características y estudiamos la fiabilidad del sistema para medir parámetros anormales de la respiración (taquipnea, bradipnea y retención de la respiración). Por último, realizamos mediciones en pacientes post-COVID-19, algunos de ellos con alteraciones respiratorias después de más de un año del diagnóstico. Los resultados experimentales mostraron que el sistema propuesto podría utilizarse potencialmente para medir la dinámica respiratoria en el ámbito clínico. Además, mientras que la FR puede calcularse fácilmente con cualquier sensor, otros parámetros deben medirse con un sensor en una posición determinada.

Health-related quality of life of women with breast cancer being treated with hormone therapy: A scoping review.

OBJECTIVE: To map the evidence available in the literature on the health-related quality of life of women with breast cancer using hormone therapy. DATA SOURCES: This review followed the Joanna Briggs Institute methodological recommendations and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews reporting guidelines. Searches were performed in nine databases using descriptors, synonyms and keywords; grey literature was also included. The review protocol was registered with the Open Science Framework under doi: http://doi.org/10.17605/OSF.IO/347FM. Inclusion criteria were established according to the Population, Concept, and Context strategy. The selection of studies was performed by two independent reviewers with the aid of RAYYAN software and disagreements were resolved by a third reviewer. The main information from the included articles was grouped into textual categories and presented by means of a narrative synthesis. DATA SUMMARY: A total of 5419 records were identified, of which 42 studies fully met the eligibility criteria. Most were multicenter studies (42.9%) and randomized controlled trials (62%). Most studies addressed anastrozole (39.5%), letrozole (34.2%), and tamoxifen (26.3%), which were studied alone or in combination. The most widely used health-related quality-of-life assessment tool was the EORTC-QLQ-C30. The concomitant use of hormone therapy and cyclin-dependent kinase inhibitors 4 and 6 showed improvement in health-related quality of life. CONCLUSION: In recent years there has been an increase in studies focused on health-related quality of life, and the evidence pointed to relevant information on health-related quality of life and the use of endocrine therapy, tamoxifen in combination with aromatase inhibitors, as well as aromatase inhibitor alone and the use of cyclin-dependent kinase 4 and 6.

Imaging evaluation of patellofemoral joint instability: a review.

The multifactorial origin of anterior knee pain in patellofemoral joint disorders leads to a demanding diagnostic process. Patellofemoral misalignment is pointed out as one of the main causes of anterior knee pain. The main anatomical risk factors of patellofemoral instability addressed in the literature are trochlear dysplasia, abnormal patellar height, and excessive tibial tubercle-trochlear groove distance. Diagnostic imaging of the patellofemoral joint has a fundamental role in assessing these predisposing factors of instability. Extensive work is found in the literature regarding the assessment of patellofemoral instability, encompassing several metrics to quantify its severity. Nevertheless, this process is not well established and standardized, resulting in some variability and inconsistencies. The significant amount of scattered information regarding the patellofemoral indices to assess the instability has led to this issue. This review was conducted to collect all this information and describe the main insights of each patellofemoral index presented in the literature. Five distinct categories were created to organize the patellofemoral instability indices: trochlear dysplasia, patellar height, patellar lateralization, patellar tilt, and tibial tubercle lateralization.

Feedback-Error Learning for time-effective gait trajectory tracking in wearable exoskeletons.

The use of exoskeletons in gait rehabilitation implies user-oriented and efficient responses of exoskeletons' controllers with adaptability for human-robot interaction. This study investigates the performance of a bioinspired hybrid control, the Feedback-Error Learning (FEL) controller, to time-effectively track user-oriented gait trajectories and adapt the exoskeletons' response to dynamic changes due to the interaction with the user. It innovates with a controller benchmarking analysis. FEL combines a proportional-integral-derivative (PID) feedback controller with a three-layer neural network feedforward controller that learns the inverse dynamics of the exoskeleton based on real-time feedback commands. FEL validation involved able-bodied subjects walking with knee and ankle exoskeletons at different gait speeds while considering gait disturbances. Results showed that the FEL control accurately (tracking error <7%) and timely (delay <30 ms) tracked gait trajectories. The feedforward controller learned the inverse dynamics of the exoskeletons in a time compliant for clinical use and adapted to variations in the gait trajectories, such as speed and position range, while the feedback controller compensated for random disturbances. FEL was more accurate and time-effective controller for tracking gait trajectories than a PID control (error <27%, delay <260 ms) and a lookup table feedforward combined with PID control (error <17%, delay >160 ms). These findings aligned with FEL's time-effectiveness favors its use in wearable exoskeletons for repetitive gait training.

Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses.

Lower limb exoskeletons and orthoses have been increasingly used to assist the user during gait rehabilitation through torque transmission and motor stability. However, the physical human-robot interface (HRi) has not been properly addressed. Current orthoses lead to spurious forces at the HRi that cause adverse effects and high abandonment rates. This study aims to assess and compare, in a holistic approach, human-robot joint misalignment and gait kinematics in three fixation designs of ankle-foot orthoses (AFOs). These are AFOs with a frontal shin guard (F-AFO), lateral shin guard (L-AFO), and the ankle modulus of the H2 exoskeleton (H2-AFO). An experimental protocol was implemented to assess misalignment, fixation displacement, pressure interactions, user-perceived comfort, and gait kinematics during walking with the three AFOs. The F-AFO showed reduced vertical misalignment (peak of 1.37 ± 0.90 cm, p-value < 0.05), interactions (median pressures of 0.39-3.12 kPa), and higher user-perceived comfort (p-value < 0.05) when compared to H2-AFO (peak misalignment of 2.95 ± 0.64 and pressures ranging from 3.19 to 19.78 kPa). F-AFO also improves the L-AFO in pressure (median pressures ranging from 8.64 to 10.83 kPa) and comfort (p-value < 0.05). All AFOs significantly modified hip joint angle regarding control gait (p-value < 0.01), while the H2-AFO also affected knee joint angle (p-value < 0.01) and gait spatiotemporal parameters (p-value < 0.05). Overall, findings indicate that an AFO with a frontal shin guard and a sports shoe is effective at reducing misalignment and pressure at the HRI, increasing comfort with slight changes in gait kinematics.

Lightweight and compact smart walking cane.

Devices such as canes and wheelchairs, used to assist locomotion, have remained mostly unchanged for centuries. Recent advances in robotics have the potential to develop smart versions of these devices that can offer better support and living conditions to their users. This is the underlying objective of this project. The existing devices, used during recovery and rehabilitation phases where gait stability is key, are often bulky and cannot be easily migrated from hospital to domestic environments, where maneuvering space tends to be restricted. This article discusses a compact, lightweight and minimally invasive, robotic cane to assist locomotion. The device can assist users with mild locomotion disabilities, e.g., in the final stages of rehabilitation, to maintain and recover their balance in standing and walking situations. This extends previous experiments with alternative control strategies, merged with indicators (based on the Gini index) able to recognize differences between users. Several experiments, with a range of users possessing different mobility impairments, confirm the viability of the robotic cane, with users comfortably using the cane after three minutes, on average, proving its ease of use and low intrusiveness, and with constant support offered during the whole movement. Furthermore, the real-time tuning of the controller gains, via the Gini inequality index, enables adjustment to the user's movement.

Provoking Artificial Slips and Trips towards Perturbation-Based Balance Training: A Narrative Review.

Humans' balance recovery responses to gait perturbations are negatively impacted with ageing. Slip and trip events, the main causes preceding falls during walking, are likely to produce severe injuries in older adults. While traditional exercise-based interventions produce inconsistent results in reducing patients' fall rates, perturbation-based balance training (PBT) emerges as a promising task-specific solution towards fall prevention. PBT improves patients' reactive stability and fall-resisting skills through the delivery of unexpected balance perturbations. The adopted perturbation conditions play an important role towards PBT's effectiveness and the acquisition of meaningful sensor data for studying human biomechanical reactions to loss of balance (LOB) events. Hence, this narrative review aims to survey the different methods employed in the scientific literature to provoke artificial slips and trips in healthy adults during treadmill and overground walking. For each type of perturbation, a comprehensive analysis was conducted to identify trends regarding the most adopted perturbation methods, gait phase perturbed, gait speed, perturbed leg, and sensor systems used for data collection. The reliable application of artificial perturbations to mimic real-life LOB events may reduce the gap between laboratory and real-life falls and potentially lead to fall-rate reduction among the elderly community.

A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons.

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users' LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices' control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.

Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review.

This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating the technical and clinical specifications of biofeedback systems (BSs), including the complementary use with assistive devices and/or physiotherapist-oriented cues. A literature search was conducted from January 2019 to September 2022 on Cochrane, Embase, PubMed, PEDro, Scopus, and Web of Science databases. Data regarding technical (sensors, biofeedback parameters, actuators, control strategies, assistive devices, physiotherapist-oriented cues) and clinical (participants' characteristics, protocols, outcome measures, BSs' effects) specifications of BSs were extracted from the relevant studies. A total of 31 studies were reviewed, which included 660 stroke survivors. Most studies reported visual biofeedback driven according to the comparison between real-time kinetic or spatiotemporal data from wearable sensors and a threshold. Most studies achieved statistically significant improvements on sensor-based and clinical outcomes between at least two evaluation time points. Future research should study the effectiveness of using multiple wearable sensors and actuators to provide personalized biofeedback to users with multiple sensorimotor deficits. There is space to explore BSs complementing different assistive devices and physiotherapist-oriented cues according to their needs. There is a lack of randomized-controlled studies to explore post-stroke stage, mental and sensory effects of BSs.

A multi-camera and multimodal dataset for posture and gait analysis.

Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.

Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors.

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients' energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data.

Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline's processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.

Inertial Data-Based AI Approaches for ADL and Fall Recognition.

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset's lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.

Fall Risk Assessment Using Wearable Sensors: A Narrative Review.

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

Does Gait with an Ankle Foot Orthosis Improve or Compromise Minimum Foot Clearance?

The purpose of this study was to investigate if the use of an ankle foot orthosis in passive mode (without actuation) could modify minimum foot clearance, and if there are any compensatory mechanisms to enable these changes during treadmill gait at a constant speed. Eight participants walked on an instrumented treadmill without and with an ankle foot orthosis on the dominant limb at speeds of 0.8, 1.2, and 1.6 km/h. For each gait cycle, the minimum foot clearance and some gait linear kinematic parameters were calculated by an inertial motion capture system. Additionally, maximum hip and knee flexion and maximum ankle plantar flexion were calculated. There were no significant differences in the minimum foot clearance between gait conditions and lower limbs. However, differences were found in the swing, stance and step times between gait conditions, as well as between limbs during gait with orthosis (p < 0.05). An increase in hip flexion during gait with orthosis was observed for all speeds, and different ankle ranges of motion were observed according to speed (p < 0.05). Thus, the use of an ankle foot orthosis in passive mode does not significantly hinder minimum foot clearance, but can change gait linear and angular parameters in non-pathological individuals.

Vibrotactile biofeedback devices in Parkinson's disease: a narrative review.

Parkinson's disease (PD) is often associated with a vast list of gait-associated disabilities, for which there is still a limited pharmacological/surgical treatment efficacy. Therefore, alternative approaches have emerged as vibrotactile biofeedback systems (VBS). This review aims to focus on the technologies supporting VBS and identify their effects on improving gait-associated disabilities by verifying how VBS were applied and validated with end-users. It is expected to furnish guidance to researchers looking to enhance the effectiveness of future vibrotactile cueing systems. The use of vibrotactile cues has proved to be relevant and attractive, as positive results have been obtained in patients' gait performance, suitability in any environment, and easy adherence. There seems to be a preference in developing VBS to mitigate freezing of gait, to improve balance, to overcome the risk of fall, and a prevalent use to apply miniaturized wearable actuators and sensors. Most studies implemented a biofeedback loop able to provide rescue strategies during or after the detection of a gait-associated disability. However, there is a need of more clinical evidence and inclusion of experimental sessions to evaluate if the biofeedback was effectively integrated into the patients' motor system.