Limb Acceleration Research Articles

Multivariable model for gait pattern differentiation in elderly patients with hip and knee osteoarthritis: A wearable sensor approach

BackgroundHip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data. MethodParticipants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features. FindingsWe included 57 knee OA patients (30 females, median age 68 [range 49–89], median BMI 29.7 [range 21.0–45.9]) and 42 hip OA patients (19 females, median age 70 [range 47–89], median BMI 28.3 [range 20.4–37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively. InterpretationThe study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

A compact setup for behavioral studies measuring limb acceleration

Behavioral studies contribute largely to a broader understanding of human brain mechanisms and the process of learning and memory. An established method to quantify motor learning is the analysis of thumb activity. In combination with brain stimulation, the effect of various treatments on neural plasticity and motor learning can be assessed. So far, the setups for thumb abduction measurements employed consist of bulky amplifiers and digital-to-analog devices to record the data. We developed a compact hardware setup to measure acceleration data which can be integrated into a wearable, including a sensor board and a microcontroller board which can be connected to a PC via USB. Additionally, we provide two software packages including graphical user interfaces, one to communicate with the hardware and one to evaluate and process the data. This work demonstrates the construction and application of our setup at the example of thumb acceleration measurement with a custom made glove and its use for research. Using integrated circuits, the size of the measurement devices is reduced to this wearable. It is simple to construct and can be operated easily by non-technical staff.

Limb Accelerations Measure on Admission to Inpatient Rehabilitation as Predictors of Daily Stepping Six-Months Following Spinal Cord Injury

Limb Accelerations Measure on Admission to Inpatient Rehabilitation as Predictors of Daily Stepping Six-Months Following Spinal Cord Injury

Higher Values of Force and Acceleration in Rear Cross Than Lead Jab: Differences in Technique Execution by Boxers

Background: Boxing, a globally popular combat sport, demands technical precision and powerful strikes at the same time. The kinetic assessment of straight punches, specifically the rear cross and lead jab, is crucial for understanding the biomechanical factors influencing punch effectiveness. This study aims to explore the kinetic properties of these punches in trained boxers, focusing on punch force, acceleration, and the concept of a proximal-to-distal pattern. Methods: Thirteen advanced-level male boxers (body weight 90.6 ± 19.2 kg, height 184.0 ± 7.4 cm, experience 9.5 ± 6.5 y) from local clubs participated in this study. Using a force plate and wireless IMU sensors, we recorded punch force and limb acceleration during the execution of rear-cross and lead-jab punches. Data analysis involved statistical tests to compare the kinetic differences (Mann–Whitney U-test) between the two punch types and assessment of the influence of body mass and training tenure on punch effectiveness (multiple regression analysis). Significant differences were observed between the rear cross and lead jab in terms of total ground reaction force (x¯ = 1709.28 N vs. x¯ = 1176.55 N), acceleration of the fist (x¯ = 94.33 m/s2 vs. x¯ = 66.07 m/s2), forearm (x¯ = 67.11 m/s2 vs. x¯ = 41.62 m/s2) and arm (x¯ = 88.40 m/s2 vs. x¯ = 81.36 m/s2), and target contact time (x¯ = 0.03 s vs. 0.02 s). The rear-cross punch exhibited higher kinetic values, indicating greater effectiveness. Additionally, body mass and training tenure were identified as significant factors influencing punch force (R2 score = 0.640). Conclusions: This study confirmed the biomechanical superiority of the rear cross over the lead jab in terms of generated force among trained boxers. The findings highlight the importance of coordination between each segment’s acceleration to generate a powerful strike. These insights are valuable for coaches and athletes in optimizing training strategies for boxing.

Does hearing impairment affect gait under realistic conditions?

Hearing loss is associated with increased risk of falling, but little is known about how hearing impairment might affect mobility. Here, we report preliminary results from an ongoing experimental study investigating dynamic properties of gait while walking with and without impaired hearing. Young- and middle-aged adult participants with self-reported normal hearing completed four walking tasks, 2 indoors, 2 outdoors, each conducted with and without a simulated hearing loss. In the impaired condition, participants wore an insert earplug in one ear combined with binaural, circumaural noise-damping headphones. We quantified gait parameters using data from inertial measurement units (IMUs) affixed to participants’ ankles and waist, allowing participants to walk farther than in typical gait assessments. Parameters included standard gait metrics, such as limb acceleration as well as a novel spatiotemporal index quantifying variability in step patterns. Data have been collected from 13 of 30 projected participants. Analysis will focus on how temporal-spatial gait parameters and variability differ between indoor and outdoor walking with and without hearing impairment. We expect gait parameters to reflect a more variable and potentially less mobile walking strategy in the outdoor condition and with impaired hearing due to increased cognitive load and/or reduced spatial awareness in those conditions.

QUANTIFYING VARIABILITY IN DAILY ACCELERATIONS RECORDED BY INERTIAL SENSORS IN HEALTHY INDIVIDUALS: IMPLICATIONS FOR GAIT MEASUREMENT IN FREE-LIVING ENVIRONMENTS

Gait measurements can vary due to various intrinsic and extrinsic factors, and this variability becomes more pronounced using inertial sensors in a free-living environment. Therefore, identifying and quantifying the sources of variability is essential to ensure measurement reliability and maintain data quality.This study aimed to determine the variability of daily accelerations recorded by an inertial sensor in a group of healthy individuals. Ten participants, four males and six females, with a mean age of 50 years (range: 29–61) and BMI of 26.9 kg/m2 (range: 21.4–36.8), were included. A single accelerometer continuously recorded lower limb accelerations over two weeks. We extracted and analyzed the accelerations of three consecutive strides within walking bouts if the time difference between the bouts was more than two hours. Multivariate mixed-effects modeling was performed on both the discretized acceleration waveforms at 101 points (0–100) and the harmonics of the signals in the frequency domain to determine the variance components for different subjects, days, bouts, and steps as the random effect variables. Intraclass correlation coefficients (ICCs) were calculated for between-day, between-bout, and between-step comparisons.The results showed that the ICCs for the between-day, between-bout, and between-step comparisons were 0.73, 0.82, 0.99 for the vertical axis; 0.64, 0.75, 0.99 for the anteroposterior axis; and 0.55, 0.96, 0.97 for the mediolateral axis. For the signal harmonics, the respective ICCs were 0.98, 0.98, 0.99 for the vertical axis; 0.54, 0.93, 0.98 for the anteroposterior axis; and 0.69, 0.78, 0.95 for the mediolateral axis.Overall, this study demonstrated that accelerations recorded continuously for multiple days in a free-living environment exhibit high variability, mainly between days, and some variability arising from differences between walking bouts during different times within days. However, reliable and repeatable gait measurements can be obtained by identifying and quantifying the sources of variability.

Assessment and indicators of kinematic behavior and perceived fatigability.

The objective of this study was to investigate the relationship between upper limb kinetics and perceived fatigability in elderly individuals during an upper limb position sustained isometric task. A total of 31 elderly participants, 16 men (72.94±4.49 years) and 15 women (72.27±6.05 years), performed a upper limb position sustained isometric task. Upper-limb acceleration was measured using an inertial measurement unit. Perceived fatigability was measured using the Borg CR10 scale. Higher mean acceleration in the x-axis throughout the activity was associated with higher final perceived fatigability scores. Moderate correlations were observed between perceived fatigability variation and mean acceleration cutoffs in all axes during the second half of the activity. In women, significant correlations were found between all perceived fatigability cutoffs and mean acceleration in the y- and x-axes. However, in men, the relationships between perceived fatigability variation and mean acceleration were more extensive and stronger. The acceleration pattern of the upper limb is linked to perceived fatigability scores and variation, with differences between sexes. Monitoring upper limb acceleration using a single inertial measurement unit can be a useful and straightforward method for identifying individuals who may be at risk of experiencing high perceived fatigability or task failure.

ANÁLISE CINEMÁTICA DA INFLUÊNCIA MUSICAL SOBRE A EXPRESSÃO CORPORAL

ABSTRACT Introduction Dance is a combination of strength and beauty. Improving the expression of body movements in dance can improve the artistic quality of the performers and provide a greater visual experience to its spectators. Objective Explore the influence of music on dancers’ upper limb movement expression through kinematic analysis based on movement mechanics. Methods The factors influencing the expression of body movements and sports training in the dance process were investigated via a questionnaire. A group of 20 volunteers performed a movement performance only through a specific rhythm, while the experimental group combined the full music with the dance moves. After a set of four dance movements, the completion time, trajectory length, speed, and acceleration of the upper limbs were recorded and rated, analyzing the fluency of the two movement groups. Results Dance movement does not interfere as much with the rhythmic control of professional dancers; however, it impacts their fluency, range, and motion. Conclusion With the cooperation of music, the dancers’ movements were more harmonious and smoother, bringing a better expressive effect to the upper limbs. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

A virtual reality physical activity pattern assessment: Mixed crossover experiments and cluster analysis.

The subjects' physical activity levels and enjoyment of exercise after 15 min of virtual reality (VR) physical activity of different intensities were compared. Thirty-two subjects were selected for a mixed crossover experiment. They were randomly assigned to exercise in three VR games with different exercise intensities. Acceleration data of the subjects were collected and subjects' exercise enjoyment and exercise levels were compared. The subjects' emotional efficacy and arousal during exercise were measured and evaluated using the Feeling Scale (FS) and the Felt Arousal Scale (FAS), and the acceleration data were evaluated by clustering using the fuzzy c-mean (FCM) clustering algorithm. A one-way ANOVA was performed on FS and FAS before and after VR physical activity, P overall p = .003 in FS, before and after low-intensity (LI), medium-intensity (MI), and high-intensity (HI) VR physical activity, the p-values were.087, p = .027, and p = .021, respectively. p < .001 in FAS, before and after LI, MI, and HI VR physical activity, the p-values were .029, < .001, < .001. According to the FCM clustering of acceleration activity counts by LI, MI, and HI, the clustering centers of the right arm acceleration counts were 2016.77, 6118.31, and 9923.45; the clustering centers of the right thigh acceleration counts were 248.30, 1895.22, and 3485.60; and the clustering centers of the combined upper and lower limb acceleration counts were 1443.83, 4415.47, and 7149.13. VR physical activity enhances subjects' sense of enjoyment of exercise and emotional arousal, with moderate intensity VR physical activity having the best effect. VR physical activity is skewed toward high upper-extremity activity and low lower-extremity activity. The combined intensity of VR physical activity matches that of traditional exercise, and it can achieve the workout effect of the traditional workout modality.

Limb accelerations during sleep are related to measures of strength, sensation, and spasticity among individuals with spinal cord injury

BackgroundTo evaluate the relationship between measures of neuromuscular impairment and limb accelerations (LA) collected during sleep among individuals with chronic spinal cord injury (SCI) to provide evidence of construct and concurrent validity for LA as a clinically meaningful measure.MethodsThe strength (lower extremity motor score), sensation (summed lower limb light touch scores), and spasticity (categorized lower limb Modified Ashworth Scale) were measured from 40 adults with chronic (≥ 1 year) SCI. Demographics, pain, sleep quality, and other covariate or confounding factors were measured using self-report questionnaires. Each participant then wore ActiGraph GT9X Link accelerometers on their ankles and wrist continuously for 1–5 days to measure LA from movements during sleep. Regression models with built-in feature selection were used to determine the most relevant LA features and the association to each measure of impairment.ResultsLA features were related to measures of impairment with models explaining 69% and 73% of the variance (R²) in strength and sensation, respectively, and correctly classifying 81.6% (F1-score = 0.814) of the participants into spasticity categories. The most commonly selected LA features included measures of power and frequency (frequency domain), movement direction (correlation between axes), consistency between movements (relation to recent movements), and wavelet energy (signal characteristics). Rolling speed (change in angle of inclination) and movement smoothness (median crossings) were uniquely associated with strength. When LA features were included, an increase of 72% and 222% of the variance was explained for strength and sensation scores, respectively, and there was a 34% increase in spasticity classification accuracy compared to models containing only covariate features such as demographics, sleep quality, and pain.ConclusionLA features have shown evidence of having construct and concurrent validity, thus demonstrating that LA are a clinically-relevant measure related to lower limb strength, sensation, and spasticity after SCI. LA may be useful as a more detailed measure of impairment for applications such as clinical prediction models for ambulation.

Music-based intervention drives paretic limb acceleration into intentional movement frequencies in chronic stroke rehabilitation.

This study presented a novel kinematic assessment of paretic limb function “online” during the actual therapeutic exercisers rooted within the acceleration domain. Twenty-eight patients at chronic stroke stages participated in an auditory-motor intervention mapping reaching movements of the paretic arm unto surfaces of large digital musical instruments and sound tablets that provided rhythmic entrainment cues and augmented auditory feedback. Patients also wore a tri-axial accelerometer on the paretic limb during the nine-session intervention. The resulting acceleration profiles were extracted and quantified within the frequency domain. Measures of peak power and peak width were leveraged to estimate volitional control and temporal consistency of paretic limb movements, respectively. Clinical assessments included the Wolf Motor Function Test and Fugl-Meyer – Upper Extremity subtest. The results showed that peak power increased significantly from Session 1 to Session 9 within oscillatory frequency ranges associated with intentional movement execution (i.e., 4.5 Hz). Decreases in peak width over time provided additional evidence for improved paretic arm control from a temporal perspective. In addition, Peak width values obtained in Session 1 was significantly correlated with pre-test Fugl-Meyer – Upper Extremity scores. These results highlighted improvements in paretic limb acceleration as an underlying mechanism in stroke motor recovery and shed further light on the utility of accelerometry-based measures of paretic limb control in stroke rehabilitation.The data reported here was obtained from a larger clinical trial: https://clinicaltrials.gov/ct2/show/NCT03246217 ClinicalTrials.gov Identifier: NCT03246217.

Identifying and modulating distinct tremor states through peripheral nerve stimulation in Parkinsonian rest tremor

BackgroundResting tremor is one of the most common symptoms of Parkinson’s disease. Despite its high prevalence, resting tremor may not be as effectively treated with dopaminergic medication as other symptoms, and surgical treatments such as deep brain stimulation, which are effective in reducing tremor, have limited availability. Therefore, there is a clinical need for non-invasive interventions in order to provide tremor relief to a larger number of people with Parkinson’s disease. Here, we explore whether peripheral nerve stimulation can modulate resting tremor, and under what circumstances this might lead to tremor suppression.MethodsWe studied 10 people with Parkinson’s disease and rest tremor, to whom we delivered brief electrical pulses non-invasively to the median nerve of the most tremulous hand. Stimulation was phase-locked to limb acceleration in the axis with the biggest tremor-related excursion.ResultsWe demonstrated that rest tremor in the hand could change from one pattern of oscillation to another in space. Median nerve stimulation was able to significantly reduce (− 36%) and amplify (117%) tremor when delivered at a certain phase. When the peripheral manifestation of tremor spontaneously changed, stimulation timing-dependent change in tremor severity could also alter during phase-locked peripheral nerve stimulation.ConclusionsThese results highlight that phase-locked peripheral nerve stimulation has the potential to reduce tremor. However, there can be multiple independent tremor oscillation patterns even within the same limb. Parameters of peripheral stimulation such as stimulation phase may need to be adjusted continuously in order to sustain systematic suppression of tremor amplitude.

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease.

Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson’s disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.

Kinematic Accelerometry Assessment Of Music-Based Motor Training in Stroke Rehabilitation

<h3>Research Objectives</h3> To use a novel limb acceleration measure to reveal mechanisms of improved paretic limb control following music-based rehabilitation. <h3>Design</h3> A consecutive sample. <h3>Setting</h3> A music and health research facility in Toronto, CA. <h3>Participants</h3> 30 community dwellers aged 30-79 years in the chronic stroke phase with minimal volitional control of the affected limb. <h3>Interventions</h3> Nine training sessions thirty-minutes in length were administered three times per week by a Neurologic Music Therapist. The music-based training incorporated mapping rhythmically cued functional arm movements onto digital sonification touch tablets. The paretic limb's acceleration profile was recorded at 1000 Hz from a wrist-worn accelerometer. <h3>Main Outcome Measures</h3> Estimates of power-spectral-density were obtained with an approximately 1-s sliding window and a 50% window overlap across the entire training session. The power-spectral-density was computed as the median of all the windows within each trace. The dominant peak in the function was then identified and quantified as the height of the peak (i.e., peak-power). Peak-power was then used to quantify functional changes in limb control across sessions. <h3>Results</h3> Repeated-measures ANOVA for peak-power revealed a main effect of session (p < .001) and follow-up trend analyses revealed a significant linear trend, highlighting continuous motor improvement across all sessions. T-tests were then used to determine that movements of the paretic limb increased in peak power (p = .001; 95% CI = -.0002, - .0007) from session 1 (M = .00004) to session 9 (M = .0002) with peak-power being localized within the lower frequency spectrum (i.e., 4.4 Hz). <h3>Conclusions</h3> Peak-power increases within the lower frequency spectrum indicates improved volitional motor control and intentionality of paretic arm movements. Such functional changes in limb acceleration represent a mechanism underlying improved motor outcomes in music-based stroke interventions. <h3>Author(s) Disclosures</h3> The authors have nothing to disclose.

Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits. However, it is difficult to differentiate gait phases outside gait laboratories, thus, this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network (ANN). Motion sensors were used to acquire the acceleration of lower limbs, and force sensitive resistors were used to detect contact state and force between the foot and the ground. Walking was recorded using a high-speed camera. Two feed forward back-propagation (BP) neural networks were developed. The resilient BP algorithm was used to train ANN. A total of 66 volunteers participated in this study. For the stance and swing phase detection, simulation of the training data showed an accuracy of 98.0 ​%. The data from the test set showed a recognition accuracy of 97.75 ​%. Because the ending point of the last phase ‘Terminal Swing’ is always 100 ​% GC, we only listed seven phases. The prediction accuracy of seven phases were: 35.9 ​%, 63.8 ​%, 93.6 ​%, 94.9 ​%, 94.8 ​%, 97.9 ​% and 98 ​% using the limb acceleration data only. The average accuracy for seven phases were 68 ​%, 91.3 ​%, 97.8 ​%, 98.9 ​%, 98.8 ​%, 99.1 ​%, and 99.5 ​% using the limb acceleration and foot pressure data for fast, normal, and slow gait speeds. This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN, which may make gait phase analysis possible under free-living conditions.

Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Through the Inclusion of Limb Accelerations During Sleep and Personal Factors

ObjectiveTo determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models. DesignCross-sectional, 1-5 days of data collection. SettingCommunity-based data collection. ParticipantsAdults with chronic (>1 year), motor incomplete SCI (N=27). InterventionsNot applicable. Main Outcome MeasuresAmbulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results. ResultsThe combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models. ConclusionsThe addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.

Human Posture Detection Method Based on Wearable Devices

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

SUMMARYOver the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

Abstract 14089: Reduced Pain External Defibrillation (RPD) and MRI-conditional RPD: Reduced Pain ind Equivalent Efficiency Validation in Swine

Introduction: External defibrillators are used for cardioversion and resuscitation after sudden cardiac arrest (SCA). External defibrillators are also required for emergency MRI (acute stroke, spinal trauma). Low-power (9 Joule) ICD RPDs [1], and MRI-conditional external defibrillator prototypes exist [2]. An RPD external defibrillator was constructed, consisting of a Zoll defibrillator integrated with a tetanizing unit. The tetanizing waveform slowly compressed chest musculature prior to the strong biphasic defibrillating pulse, reducing chest contraction during the biphasic pulse, the major pain source. This RPD system (Fig. 1A-D) was evaluated for pain reduction and defibrillation effectiveness in swine. Method: The tetanizing unit consisted of a programmable generator that delivered a triangular 1-KHz pulse of 250-2000msec duration and 10-100 Volt peak amplitude, and subsequently triggered the conventional defibrillator to send out standard short (8msec) powerful (20-400 J) biphasic pulses. Forward limb motion (Fig. 1E), an established pain measure [3], was evaluated by measuring limb acceleration, acceleration rate and work (energy). 5 swine were arrested electrically and then defibrillated. RPD was repeated 15-20 times/swine, varying tetanizing parameters and biphasic energy. Results: Fig. 1F-H compare an RPD defibrillation and equivalent biphasic defibrillation, showing smaller accelerations and acceleration rates. Fig. 1J shows work results, at 30-200J biphasic energy, demonstrating an 83 + 15% limb work reduction with the RPD waveforms. Optimal tetanizing parameters were 15-25V amplitude and 500-750msec duration. Rhythm recovery for RPD and conventional defibrillation was identical. Conclusions: Reduced pain defibrillation may allow cardioversion without anesthesia and faster defibrillation after SCA.

A wearable motion capture device able to detect dynamic motion of human limbs

Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.