A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor.

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.

Increased body movement equals better performance? Not always! Musical style determines motion degree perceived as optimal in music performance.

Musicians' body behaviour has a preponderant role in audience perception. We investigated how performers' motion is perceived depending on the musical style and musical expertise. To further explore the effect of visual input, stimuli were presented in audio-only, audio-visual and visual-only conditions. We used motion and audio recordings of expert saxophone players playing two contrasting excerpts (positively and negatively valenced). For each excerpt, stimuli represented five motion degrees with increasing quantity of motion (QoM) and distinct predominant gestures. In the experiment (online and in-person), 384 participants rated performance recordings for expressiveness, professionalism and overall quality. Results revealed that, for the positively valenced excerpt, ratings increased as a function of QoM, whilst for the negatively valenced, the recording with predominant flap motion was favoured. Musicianship did not have a significant effect in motion perception. Concerning multisensory integration, both musicians and non-musicians presented visual dominance in the positively valenced excerpt, whereas in the negatively valenced, musicians shifted to auditory dominance. Our findings demonstrate that musical style not only determines the way observers perceive musicians' movement as adequate, but also that it can promote changes in multisensory integration.

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.

Prospects and challenges in using neuronal extracellular vesicles in biomarker research.

Extracellular vesicles (EVs) hold promise as a source of disease biomarkers. The diverse molecular cargo of EVs can potentially indicate the status of their tissue of origin, even against the complex background of whole plasma. The main tools currently available for assessing biomarkers of brain health include brain imaging and analysis of the cerebrospinal fluid of patients. Given the costs and difficulties associated with these methods, isolation of EVs of neuronal origin (NEVs) from the blood is an attractive approach to identify brain-specific biomarkers. This perspective describes current key challenges in EV- and NEV-based biomarker research. These include the relative low abundance of EVs, the lack of validated isolation methods, and the difficult search for an adequate target for immunocapturing NEVs. We discuss that these challenges must be addressed before NEVs can fulfill their potential for biomarker research. HIGHLIGHTS: NEVs are promising sources of biomarkers for brain disorders. Immunocapturing NEVs from complex biofluids presents several challenges. The choice of surface target for capture will determine NEV yield. Contamination by non-EV sources is relevant for biomarkers at low concentrations.

Characterization of the chemoreceptor repertoire of a highly specialized fly with comparisons to other Drosophila species.

To explore the diversity of scenarios in nature, animals have evolved tools to interact with different environmental conditions. Chemoreceptors are an important interface component and among them, olfactory receptors (ORs) and gustatory receptors (GRs) can be used to find food and detect healthy resources. Drosophila is a model organism in many scientific fields, in part due to the diversity of species and niches they occupy. The contrast between generalists and specialists Drosophila species provides an important model for studying the evolution of chemoreception. Here, we compare the repertoire of chemoreceptors of different species of Drosophila with that of D. incompta, a highly specialized species whose ecology is restricted to Cestrum flowers, after reporting the preferences of D. incompta to the odor of Cestrum flowers in olfactory tests. We found evidence that the chemoreceptor repertoire in D. incompta is smaller than that presented by species in the Sophophora subgenus. Similar patterns were found in other non-Sophophora species, suggesting the presence of underlying phylogenetic trends. Nevertheless, we also found autapomorphic gene losses and detected some genes that appear to be under positive selection in D. incompta, suggesting that the specific lifestyle of these flies may have shaped the evolution of individual genes in each of these gene families.

Transcriptomic analysis of plasma exosomes provides molecular information of response to cabazitaxel treatment in men with metastatic castration-resistant prostate cancer.

BACKGROUND: Prostate cancer is the second most common cancer type and the second most common cancer-related cause of death in men. Cabazitaxel, a next-generation taxane, shows favorable toxicity profile and is effective in docetaxel-resistant tumors. Despite initial responses, in most cases, prostate cancer patients acquire resistance to cabazitaxel. There is a need to identify molecular markers that can monitor and predict treatment response. METHODS: We performed transcriptional exosome profiling (Human Transcriptome Array-HTA 2.0) from the plasma of 19 patients with castration-resistant prostate cancer at baseline and in patients after one cycle of cabazitaxel (C1). The patients were stratified in two groups (responders and nonresponders) according to their clinical response to cabazitaxel. Gene set enrichment analysis and ingenuity pathway analysis platforms were used for gene and pathway analysis. RESULTS: We detected molecular differences in the exosomes from two groups of patients (nonresponders vs. responders) at baseline in pathways related to prostate cancer, oncogenic signaling, cytoskeleton, and immune system. In nonresponders, we found enrichment of cytoskeleton related gene (Stathmin-1 and ITSN1) that have been associated with resistance to cabazitaxel. Monitoring of exosomal transcripts after the first cycle of treatment revealed changes in pathways associated with response to treatment. CONCLUSIONS: Sequential transcriptional profiling of plasma-derived exosomes reveals differential expression of genes that may reflect resistance to cabazitaxel treatment and therapy response.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

Heart rate response to cortical arousals in patients with isolated obstructive sleep apnea and with comorbid insomnia (COMISA).

PURPOSE: Comorbid insomnia often occurs in patients with obstructive sleep apnea (OSA), referred to as COMISA. Cortical arousals manifest as a common feature in both OSA and insomnia, often accompanied by elevated heart rate (HR). Our objective was to evaluate the heart rate response to nocturnal cortical arousals in patients with COMISA and patients with OSA alone. METHODS: We analyzed data from patients with COMISA and from patients with OSA matched for apnea-hypopnea index. Sleep staging and analysis of respiratory events and cortical arousals were performed using the Philips Somnolyzer automatic scoring system. Beat-by-beat HR was analyzed from the onset of the cortical arousal to 30 heartbeats afterwards. HR responses were divided into peak and recovery phases. Cortical arousals were separately evaluated according to subtype (related to respiratory events and spontaneous) and duration (3-6 s, 6-10 s, 10-15 s). RESULTS: A total of 72 patients with COMISA and 72 patients with OSA were included in this study. There were no overall group differences in the number of cortical arousals with and without autonomic activation. No significant differences were found for spontaneous cortical arousals. The OSA group had more cortical arousals related to respiratory events (21.0 [14.8-30.0] vs 16.0 [9.0-27.0], p = 0.016). However, the COMISA group had longer cortical arousals (7.2 [6.4-7.8] vs 6.7 [6.2-7.7] s, p = 0.024) and the HR recovery phase was prolonged (52.5 [30.8-82.5] vs 40.0 [21.8-55.5] beats/min, p = 0.017). Both the peak and the recovery phase for longer cortical arousals with a duration of 10-15 s were significantly higher in patients with COMISA compared to patients with OSA (47.0 [27.0-97.5] vs 34.0 [21.0-71.0] beats/min, p = 0.032 and 87.0 [47.0-132.0] vs 71.0 [43.0-103.5] beats/min, p = 0.049, respectively). CONCLUSIONS: The HR recovery phase after cortical arousals related to respiratory events is prolonged in patients with COMISA compared to patients with OSA alone. This response could be indicative of the insomnia component in COMISA.

Swimming sprint performance depends on upper/lower limbs strength and swimmers level.

Swimming performance is likely influenced by strength, but differences between butterfly, backstroke, breaststroke and front crawl, as well as between novice and expert swimmers, are unclear. We have examined the associations between sprint performances, upper and lower limb strength, and anthropometric characteristics in 14 (six males and eight females) non-elite and 16 (nine males and seven females) elite-level swimmers. After an anthropometric characterisation, participants performed four 25 m maximal swims (one per technique) with 10 min intervals, right and left shoulder flexion/extension isokinetic testing at 90 and 300º/s angular velocities and three countermovement jumps. Pearson correlation analysis showed that sprint times were moderate-largely negatively correlated with upper and lower limb strength and power (r ± 95%CI = 0.39 ± 0.26-0.77 ± 0.13, p < 0.05). Elite swimmers higher strength levels were associated with longer stroke length in butterfly and front crawl, and with higher stroke rate in backstroke and breaststroke (r ± 95%CI = 0.37 ± 0.32-0.68 ± 0.21; p < 0.05). Butterfly, backstroke and front crawl sprint times were moderate-largely negatively related with arm span (r ± 95%CI = 0.37 ± 0.26, 0.39 ± 0.25 and 0.69 ± 0.17, p < 0.05). The predictive model indicated that higher dry-land strength values distinguished elite from non-elite swimmers (r2 = 0.67-0.81; p < 0.001). This association was not observed per performance level and per sex, confirming that sprint swimming performance levels can be differentiated by dry-land strength testing.

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.

A systematic review with meta-analysis of the diagnostic test accuracy of pedicle screw electrical stimulation.

PURPOSE: To provide a systematic review with meta-analysis providing evidence of the current diagnostic test accuracy (DTA) of pedicle screw electrical stimulation. METHODS: A systematic database search on PubMed, Scopus and Web of Science was performed according to the PRISMA-DTA guidelines, and eligibility criteria applied to reduce the results to: (1) only journal articles reporting electrical stimulation of the pedicle screw head, (2) screw position confirmation by imaging techniques, and (3) enough information allowing the calculation of a 2 × 2 contingency table. Sample characteristics, image confirmation method, electrical current threshold and stimulation results were retrieved and analyzed using according to appropriate DTA analysis methods, and allowing the calculation of specificity, sensitivity for pedicle screws insertion at the lumbar and thoracic levels. RESULTS: Lumbar screw stimulation presents a higher sensitivity (0.586 [0.336, 0.798] and specificity (0.984 [0.958, 0.994]) than thoracic screws (sensitivity: 0.270 [0.096; 0.562]; specificity: 0.958 [0.931, 0.975]). The same is observed in terms of the diagnostic odds ratio for lumbar (88.32 [32.136, 242.962]) and thoracic (8.460 [2.139, 33.469]) levels. When performing a sub-group analysis, it is possible to divide the lumbar stimulation threshold as 8 and 10-12 mA, and the thoracic threshold as 6 and 9-12 mA. A threshold of 8 mA at the lumbar level provides higher sensitivity and specificity. Increasing the threshold results in higher specificity but not sensitivity. In fact, at the range of 10-12 mA, the diagnostic validity is too low to confer this technique any robust diagnostic validity. Similarly, at the thoracic level, lower threshold currents are associated with increased sensitivity, but their diagnostic validity is very low. CONCLUSION: Electrical stimulation of the pedicle screw can be used as an adequate diagnostic capability at the lumbar level with a threshold of 8 mA. However, thoracic stimulation is currently not reliable, with very low sensitivity and diagnostic validity at 6 mA or higher.

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.

Optimal multi-field functional electrical stimulation parameters for the "drinking task - reaching phase" and related upper limb kinematics repeatability in post stroke subjects.

BACKGROUND: No specific guidelines for the management of functional electrical stimulation (FES) parameters in post stroke patients have been defined yet, despite its frequent use. The purpose of this study is to characterize the optimal FES parameters that assist the reaching phase of drinking task ("drinking task - reaching phase") on post stroke subjects and to analyze the related upper limb (UL) movement quality indicators repeatability. METHODS: An observational study with a test and re-test design involving ten post stroke subjects with UL dysfunction was performed. End-point and joint kinematics of contralesional UL were assessed during the "drinking task - reaching phase" with FES through a test and retest design. FES parameters were adjusted to improve UL function according to a consensus between physiotherapists and patients' perspective. FINDINGS: It was possible to establish reliable FES parameters that assisted the "drinking task - reaching phase". All FES parameters presented high to very high repeatability and led to moderate to very high repeatability in almost UL movement quality indicators during the "drinking task - reaching phase". INTERPRETATION: These findings show that the main characteristics of FES parameters that improves patient perception of change are quite stable, which facilitate its implementation in clinical practice by allowing consistence between intervention sessions.

Can increased load carriage affect lower limbs kinematics during military gait?

The aim of this study was to investigate if increased load carriage, in male military personnel, can affect the lower limbs kinematics. Twelve male military volunteers from the Portuguese Army were recruited and evaluated in an unloaded and loaded gait condition. Linear kinematics and lower limbs joint angle at heel strike, midstance and toe off were calculated. The stance, swing and double support times were found to be different between load conditions (p < 0.05). There was an interaction between load and limb (p < 0.05) for joint angles, during midstance, with limbs performing different movements in the frontal plane during loaded gait. Load increase had a different effect on the right knee, with a reduction in the abduction (valgus). This study may be beneficial in offering suggestion to improve the performance of gait with load and in an attempt to help prevent possible injuries. Practitioner summary: Increased load can affect lower limbs of male soldiers at the pelvic, hip and knee angles on the frontal plane, which can alter the joint force distribution. While these alterations may indicate protective mechanics, load management procedures should be implemented along with gait monitoring to avoid negative effects in performance.

Optimal Multifield Functional Electrical Stimulation Parameters for the "Turn on the Light" Task and Related Upper Limb Kinematics Repeatability in Poststroke Subjects.

OBJECTIVE: To characterize the optimal functional electrical stimulation (FES) parameters that assist the turn on the light task (TOTL) on poststroke participants and to analyze the related upper limb (UL) kinematics repeatability. DESIGN: Cross-sectional study. SETTING: Human movement research center. PARTICIPANTS: Poststroke individuals (N=11) with history of a single unilateral stroke that resulted in a motor control dysfunction of the contralesional UL. INTERVENTIONS: FES based on surface multifield technology applied to the contralesional wrist and finger extensors during the TOTL. MAIN OUTCOME MEASURES: FES outcome metrics (virtual electrodes, stimulation duration, intensity) and kinematic metrics (end-point kinematics [absolute and relative duration, mean and peak velocities, relative instant of peak velocity, index of curvature, number of movement units] and joint kinematics [shoulder, elbow, wrist end position and range of movement]). Outcome measures were assessed 2 times with a 72-hour maximum time interval. CONCLUSION: It was possible to establish reliable FES parameters that assisted the TOTL on poststroke participants. These stimulation parameters led to high to very high repeatability in terms of UL kinematics for most of the cases.

An additional brain endocast of the ictidosaur Riograndia guaibensis (Eucynodontia: Probainognathia): intraspecific variation of endocranial traits.

Recently, the morphology and encephalization of the brain endocast of the Triassic non-mammaliaform probainognathian cynodont Riograndia guaibensis were studied. Here, we analyzed the brain endocast of an additional specimen of this species. The new endocast shows well-defined olfactory bulbs and a median sulcus dividing the hemispheres, traits that were not clearly observed in the first studied specimen. Encephalization quotients were also calculated, revealing similar values to other non-mammaliaform cynodonts and lower than those of the first analyzed specimen. The analyzed cranium is slightly larger than the first studied one and may represent an advanced ontogenetic stage. Hence, these differences may be related to the intraspecific variation of this cynodont or alternatively, to the preservation of each specimen.

Muscle Co-Activation around the Knee during Different Walking Speeds in Healthy Females.

The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = -0.3386, p < 0.001), single support (r = -0.2144, p < 0.01), second double support (r = -0.4949, p < 0.001), and Swing (r = -0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.

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.

Equimetrix Device: Criteria Based Validation and Reliability Analysis of the Center of Mass and Base of Support of a Human Postural Assessment System.

Human postural control is a fundamental ability for static and dynamic tasks, especially in hiper- and hipo-functional populations, such as the elderly. The Equimetrix is a clinical device developed to assess both the base of support (BoS) and the center of mass (CoM) dynamics, thus allowing their use as new evaluation and training tools. This study aims to perform a criteria based validation of Equimetrix by comparing the BoS and CoM data with gold-standard equipment. A motion capture system, force platform, and pressure mat were used to calculate the CoM, center of pressure (CoP) and BoS during bipedal, unipedal, feet together and full tandem stances. Results demonstrate an excellent reliability of Equimetrix in terms of spatial accuracy of the CoM, although over-estimating the CoM height. Differences were found when comparing Mean velocity Path with the CoM, but not with the CoP, indicating a lower reliability in time-based parameters. The Equimetrix presents a tendency to overestimate the BoS, with mixed reliability values, which may be related to the different size of sensing elements between the Equimetrix and the pressure sensing mat. These are encouraging results that should be further explored during dynamic tasks.