Walking ability of individuals fitted with transfemoral bone-anchored prostheses: A comparative study of gait parameters.

OBJECTIVE: This study presents the walking abilities of participants fitted with transfemoral bone-anchored prostheses using a total of 14 gait parameters. DESIGN: Two-centre retrospective cross-sectional comparative study. SETTING: Research facilities equipped with tridimensional motion capture systems. PARTICIPANTS: Two control arms included eight able-bodied participants arm (54 ± 9 years, 1.75 ± 0.07 m, 76 ± 7 kg) and nine participants fitted with transfemoral socket-suspended prostheses arm (59 ± 9 years, 1.73 ± 0.07 m, 80 ± 16 kg). The intervention arm included nine participants fitted with transfemoral bone-anchored prostheses arm (51 ± 13 years, 1.78 ± 0.09 m, 87.3 ± 16.1 kg). INTERVENTION: Fitting of transfemoral bone-anchored prostheses. MAIN MEASURES: Comparisons were performed for two spatio-temporal, three spatial and nine temporal gait parameters. RESULTS: The cadence and speed of walking were 107 ± 6 steps/min and 1.23 ± 0.19 m/s for the able-bodied participants arm, 88 ± 7 steps/min and 0.87 ± 0.17 m/s for the socket-suspended prosthesis arm, and 96 ± 6 steps/min and 1.03 ± 0.17 m/s for bone-anchored prosthesis arm, respectively. Able-bodied participants and bone-anchored prosthesis arms were comparable in age, height, and body mass index as well as cadence and speed of walking, but the able-bodied participant arm showed a swing phase 31% shorter. Bone-anchored and socket-suspended prostheses arms were comparable for age, height, mass, and body mass index as well as cadence and speed of walking, but the bone-anchored prosthesis arm showed a step width and duration of double support in seconds 65% and 41% shorter, respectively. CONCLUSIONS: Bone-anchored and socket-suspended prostheses restored equally well the gait parameters at a self-selected speed. This benchmark data provides new insights into the walking ability of individuals using transfemoral bionics bone-anchored prostheses.

Characterization of prosthetic knees through a low-dimensional description of gait kinematics.

The characterization of both limbs' behaviour in prosthetic gait is of key importance for improving the prosthetic components and increasing the biomechanical capability of trans-femoral amputees. When characterizing human gait, modular motor control theories have been proven to be powerful in providing a compact description of the gait patterns. In this paper, the planar covariation law of lower limb elevation angles is proposed as a compact, modular description of prosthetic gait; this model is exploited for a comparison between trans-femoral amputees walking with different prosthetic knees and control subjects walking at different speeds. Results show how the planar covariation law is maintained in prostheses users, with a similar spatial organization and few temporal differences. Most of the differences among the different prosthetic knees are found in the kinematic coordination patterns of the sound side. Moreover, different geometrical parameters have been calculated over the common projected plane, and their correlation with classical gait spatiotemporal and stability parameters has been investigated. The results from this latter analysis have highlighted a correlation with several parameters of gait, suggesting that this compact description of kinematics unravels a significant biomechanical meaning. These results can be exploited to guide the control mechanisms of prosthetic devices based purely on the measurement of relevant kinematic quantities.

A Novel Equivalent Time Sampling-Based Method for Pulse Transit Time Estimation with Applications into the Cardiovascular Disease Diagnosis.

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the present study focuses on a novel image analysis-based method for PTT estimation through the application of equivalent time sampling. The method, which post-processes color Doppler videos, was tested on two different setups: a Doppler flow phantom set in pulsatile mode and an in-house arterial simulator. In the former, the Doppler shift was due to the echogenic properties of the blood mimicking fluid only, since the phantom vessels are non-compliant. In the latter, the Doppler signal relied on the wall movement of compliant vessels in which a fluid with low echogenic properties was pumped. Therefore, the two setups allowed the measurement of the flow average velocity (FAV) and the pulse wave velocity (PWV), respectively. Data were collected through an ultrasound diagnostic system equipped with a phased array probe. Experimental outcomes confirm that the proposed method can represent an alternative tool for the local measurement of both FAV in non-compliant vessels and PWV in compliant vessels filled with low echogenic fluids.

A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue.

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2-10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.

The Influence of the sEMG Amplitude Estimation Technique on the EMG-Force Relationship.

The estimation of the sEMG-force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.

Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities.

Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation. A total of 15 healthy controls (HC) and eight people with LBP performed three lifting tasks with a progressively increasing lifting index (LI), each lasting 15 min. Bilaterally erector spinae longissimus (ESL) activity and rectus abdominis superior (RAS) were recorded using bipolar surface electromyography systems (sEMG), and the time-varying multi-muscle coactivation function (TMCf) was computed. The TMCf can significantly discriminate each pair of LI and it is higher in LBP than HC. Collectively, our findings suggest that it is possible to identify different motor strategies between people with and without LBP. The main finding shows that LBP, to counteract pain, coactivates the trunk muscles more than HC, thereby adopting a strategy that is stiffer and more fatiguing.

Global Muscle Coactivation of the Sound Limb in Gait of People with Transfemoral and Transtibial Amputation.

The aim of this study was to analyze the effect of the level of amputation and various prosthetic devices on the muscle activation of the sound limb in people with unilateral transfemoral and transtibial amputation. We calculated the global coactivation of 12 muscles using the time-varying multimuscle coactivation function method in 37 subjects with unilateral transfemoral amputation (10, 16, and 11 with mechanical, electronic, and bionic prostheses, respectively), 11 subjects with transtibial amputation, and 22 healthy subjects representing the control group. The results highlighted that people with amputation had a global coactivation temporal profile similar to that of healthy subjects. However, amputation increased the level of the simultaneous activation of many muscles during the loading response and push-off phases of the gait cycle and decreased it in the midstance and swing subphases. This increased coactivation probably plays a role in prosthetic gait asymmetry and energy consumption. Furthermore, people with amputation and wearing electronic prosthesis showed lower global coactivation when compared with people wearing mechanical and bionic prostheses. These findings suggest that the global lower limb coactivation behavior can be a useful tool to analyze the motor control strategies adopted and the ability to adapt to the prosthetic device.

Modular motor control of the sound limb in gait of people with trans-femoral amputation.

BACKGROUND: The above-knee amputation of a lower limb is a severe impairment that affects significantly the ability to walk; considering this, a complex adaptation strategy at the neuromuscular level is needed in order to be able to move safely with a prosthetic knee. In literature, it has been demonstrated that muscle activity during walking can be described via the activation of a small set of muscle synergies. The analysis of the composition and the time activation profiles of such synergies have been found to be a valid tool for the description of the motor control schemes in pathological subjects. METHODS: In this study, we used muscle synergy analysis techniques to characterize the differences in the modular motor control schemes between a population of 14 people with trans-femoral amputation and 12 healthy subjects walking at two different (slow and normal self-selected) speeds. Muscle synergies were extracted from a 12 lower-limb muscles sEMG recording via non-negative matrix factorization. Equivalence of the synergy vectors was quantified by a cross-validation procedure, while differences in terms of time activation coefficients were evaluated through the analysis of the activity in the different gait sub-phases. RESULTS: Four synergies were able to reconstruct the muscle activity in all subjects. The spatial component of the synergy vectors did not change in all the analysed populations, while differences were present in the activity during the sound limb's stance phase. Main features of people with trans-femoral amputation's muscle synergy recruitment are a prolonged activation of the module composed of calf muscles and an additional activity of the hamstrings' module before and after the prosthetic heel strike. CONCLUSIONS: Synergy-based results highlight how, although the complexity and the spatial organization of motor control schemes are the same found in healthy subjects, substantial differences are present in the synergies' recruitment of people with trans femoral amputation. In particular, the most critical task during the gait cycle is the weight transfer from the sound limb to the prosthetic one. Future studies will integrate these results with the dynamics of movement, aiming to a complete neuro-mechanical characterization of people with trans-femoral amputation's walking strategies that can be used to improve the rehabilitation therapies.

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement.

An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the chair. The position of the sensors was selected for maximizing the detection of variations of user's posture. The effectiveness of the designed system was evaluated through an experiment where increasing stress levels were obtained by administering a Stroop test. The collected results had been analyzed by considering three different time intervals based on the difficulty level of the test (low, medium, and high). A transition analysis conducted on postures assumed during the test showed that participants reached a different posture at the end of the test, when the cognitive engagement increased, with respect to the beginning. This evidence highlighted the presence of movement presumably due to the increased cognitive engagement. Overall, the performed analysis showed the proposed monitoring system could be used to identify body posture variations related to different levels of engagement of a seated user while performing cognitive tasks.

Thermal Energy Harvesting on the Bodily Surfaces of Arms and Legs through a Wearable Thermo-Electric Generator.

This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 μW. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities.

Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study.

PURPOSE: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.

Theta Burst Stimulation of the Precuneus Modulates Resting State Connectivity in the Left Temporal Pole.

It has been shown that continuous theta burst stimulation (cTBS) over the precuneus acts on specific memory retrieval abilities. In order to study the neural mechanisms beyond these findings, we combined cTBS and resting-state functional magnetic resonance imaging. Our experimental protocol involved stimulation and sham conditions on a group of healthy subjects, and each condition included a baseline and two follow-up acquisitions (5 and 15 min after baseline) after cTBS. We analysed brain functional connectivity by means of graph theoretical measures, with a specific focus on the network modular structure. Our results showed that cTBS of the precuneus selectively affects the left temporal pole, decreasing its functional connectivity in the first follow-up. Moreover, we observed a significant increase in the size of the module of the precuneus in the second follow-up. Such effects were absent in the sham condition. We observed here a modulation of functional connectivity as a result of inhibitory stimulation over the precuneus. Such a modulation first acts indirectly on the temporal area and then extends the connectivity of the precuneus itself by a feed-back mechanism. Our current findings extend our previous behavioural observations and increase our understanding of the mechanisms underlying the stimulation of the precuneus.

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis.

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that can alter cortical excitability and modulate behaviour in a polarity-dependent way. Despite the widespread use of this method in the neuroscience field, its effects on ongoing local or global (network level) neuronal activity are still not foreseeable. A way to shed light on the neuronal mechanisms underlying the cortical connectivity changes induced by tDCS is provided by the combination of tDCS with electroencephalography (EEG). In this study, twelve healthy subjects underwent online tDCS-EEG recording (i.e., simultaneous), during resting-state, using 19 EEG channels. The protocol involved anodal, cathodal and sham stimulation conditions, with the active and the reference electrodes in the left frontocentral area (FC3) and on the forehead over the right eyebrow, respectively. The data were processed using a network model, based on graph theory and the synchronization likelihood. The resulting graphs were analysed for four frequency bands (theta, alpha, beta and gamma) to evaluate the presence of tDCS-induced differences in synchronization patterns and graph theory measures. The resting state network connectivity resulted altered during tDCS, in a polarity-specific manner for theta and alpha bands. Anodal tDCS weakened synchronization with respect to the baseline over the fronto-central areas in the left hemisphere, for theta band (p<0.05). In contrast, during cathodal tDCS a significant increase in inter-hemispheric synchronization connectivity was observed over the centro-parietal, centro-occipital and parieto-occipital areas for the alpha band (p<0.05). Local graph measures showed a tDCS-induced polarity-specific differences that regarded modifications of network activities rather than specific region properties. Our results show that applying tDCS during the resting state modulates local synchronization as well as network properties in slow frequency bands, in a polarity-specific manner.

Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site.

BACKGROUND: Functional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge. METHODS: A multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove. RESULTS: The stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures. CONCLUSIONS: A subset of functionally relevant stimulation electrodes could be selected fast, automatic and non-painful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.

Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting.

In this paper, two different piezoelectric transducers-a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)-were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities-walking, walking up and down stairs, jogging and running-were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW-74 µW using both transducers for each joint.

Intermuscular coherence contributions in synergistic muscles during pedaling.

The execution of rhythmical motor tasks requires the control of multiple skeletal muscles by the Central Nervous System (CNS), and the neural mechanisms according to which the CNS manages their coordination are not completely clear yet. In this study, we analyze the distribution of the neural drive shared across muscles that work synergistically during the execution of a free pedaling task. Electromyographic (EMG) activity was recorded from eight lower limb muscles of eleven healthy untrained participants during an unconstrained pedaling exercise. The coordinated activity of the lower limb muscles was described within the framework of muscle synergies, extracted through the application of nonnegative matrix factorization. Intermuscular synchronization was assessed by calculating intermuscular coherence between pairs of EMG signals from co-active, both synergistic and non-synergistic muscles within their periods of co-activation. The spatiotemporal structure of muscle coordination during pedaling was well represented by four muscle synergies for all the subjects. Significant coherence values within the gamma band (30-60 Hz) were identified only for one out of the four extracted muscle synergies. This synergy is mainly composed of the activity of knee extensor muscles, and its function is related to the power production and crank propelling during the pedaling cycle. In addition, a significant coherence peak was found in the lower frequencies for the GAM/SOL muscle pair, possibly related to the ankle stabilizing function of these two muscles during the pedaling task. No synchronization was found either for the other extracted muscle synergies or for pairs of co-active but non-synergistic muscles. The obtained results seem to suggest the presence of intermuscular synchronization only when a functional force production is required, with the observed gamma band contribution possibly reflecting a cortical drive to synergistic muscles during pedaling.

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors.

Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.

Validity and reliability of an alternative method for measuring power output during six-second all-out cycling.

In a laboratory setting where both a mechanically-braked cycling ergometer and a motion analysis (MA) system are available, flywheel angular displacement can be estimated by using MA. The purpose of this investigation was to assess the validity and reliability of a MA method for measuring maximal power output (Pmax) in comparison with a force transducer (FT) method. Eight males and eight females undertook three identical sessions, separated by 4 to 6 days; the first being a familiarization session. Individuals performed three 6-second sprints against 50% of the maximal resistance to complete two pedal revolutions with a 3-minute rest between trials. Power was determined independently using both MA and FT analyses. Validity: MA recorded significantly higher Pmax than FT (P < .05). Bland-Altman plots showed that there was a systematic bias in the difference between the measures of the two systems. This difference increased as power increased. Repeatability: Intraclass correlation coefficients were on average 0.90 ± 0.05 in males and 0.85 ± 0.08 in females. Measuring Pmax by MA, therefore, is as appropriate for use in exercise physiology research as Pmax measured by FT, provided that a bias between these measurements methods is allowed for.

Novel Metrics for High-Density sEMG Analysis in the Time-Space Domain During Sustained Isometric Contractions.

Goal: This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. Methods: Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. Results: Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. Conclusions: These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.

Intelligent Human-Computer Interaction: Combined Wrist and Forearm Myoelectric Signals for Handwriting Recognition.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human-computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.