Application of tilt sensors in functional electrical stimulation

A Case with Left Hemiplegia after Cerebral Infarction with Improved Walking Ability Through Robot-assisted Gait Training Combined with Neuromuscular Electrical Stimulation for Foot Drop

Yamaguchi A, Kanazawa Y, Hirano S, Aoyagi Y. A Case with Left Hemiplegia after Cerebral Infarction with Improved Walking Ability Through Robot-assisted Gait Training Combined with Neuromuscular Electrical Stimulation for Foot Drop. Jpn J Compr Rehabil Sci 2024; 15: 88-93.

Background

Gait training-assist robots and neuromuscular electrical stimulation devices have been shown to be useful in gait training for patients with hemiplegia. However, no case reports have documented the combined use of a gait training-assist robot and a neuromuscular electrical stimulator for gait rehabilitation. In this study, we present the case of a patient with left hemiplegia who demonstrated remarkable improvement in walking ability after using a combination of a gait training-assist robot and a neuromuscular electrical stimulator for foot drop.

Case Presentation

A 60-year-old man developed severe left hemiplegia following a stroke in the right middle cerebral artery region. His lower limb motor function, as assessed by the Stroke Impairment Assessment Set (SIAS), was completely impaired (score of 0), and he was unable to walk by the 57th day post-onset. By the 66th day, his lower limb motor function remained unchanged (SIAS score of 0), and he frequently stumbled on his left foot at the start of the swing phase during gait training. As a result, robot-assisted gait training combined with neuromuscular electrical stimulation for foot drop was initiated. By the 88th day, his lower limb motor function improved to a score of 1 on the SIAS, and his Functional Independence Measure (FIM) walk item improved to a score of 4 with the use of an ankle-foot orthosis and a cane. On the 89th day, he transitioned to conventional therapy without the devices. By the 114th day, he was able to walk with a T-cane without the need for an orthosis.

Conclusion

The combination of a gait training-assist robot and a neuromuscular electrical stimulator for foot drop facilitated dorsiflexion of the ankle during the swing phase, allowed the patient to practice walking with minimal assistance. This promoted active patient-led walking and more efficient motor learning, ultimately leading to independent walking.

Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis

Rehabilitative and neuroprosthetic approaches after spinal cord injury (SCI) aim to reestablish voluntary control of movement. Promoting recovery requires a mechanistic understanding of the return of volition over action, but the relationship between re-emerging cortical commands and the return of locomotion is not well established. We introduced a neuroprosthesis delivering targeted bi-cortical stimulation in a clinically relevant contusive SCI model. In healthy and SCI cats, we controlled hindlimb locomotor output by tuning stimulation timing, duration, amplitude, and site. In intact cats, we unveiled a large repertoire of motor programs. After SCI, the evoked hindlimb lifts were highly stereotyped, yet effective in modulating gait and alleviating bilateral foot drag. Results suggest that the neural substrate underpinning motor recovery had traded-off selectivity for efficacy. Longitudinal tests revealed that the return of locomotion after SCI was correlated with recovery of the descending drive, which advocates for rehabilitation interventions directed at the cortical target.

Robotic Cane Controlled to Adapt Automatically to Its User Gait Characteristics

Research on robotic assistance devices tries to minimize the risk of falls due to misuse of non-actuated canes. This paper contributes to this research effort by presenting a novel control strategy of a robotic cane that adapts automatically to its user gait characteristics. We verified the proposed control law on a robotic cane sharing the main shape features of a non-actuated cane. It consists of a motorized telescopic shaft mounted on the top of two actuated wheels driven by the same motor. Cane control relies on two Inertial Measurement Units (IMU). One is attached to the cane and the other to the thigh of its user impaired leg. During the swing phase of this leg, the motor of the wheels is controlled to enable the tracking of the impaired leg thigh angle by the cane orientation. The wheels are immobilized during the stance phase to provide motionless mechanical support to the user. The shaft length is continuously adjusted to keep a constant height of the cane handle. The primary goal of this work is to show the feasibility of the cane motion synchronization with its user gait. The control strategy looks promising after several experiments. After further investigations and experiments with end-users, the proposed control law could pave the road toward its use in robotic canes used either as permanent assistance or during rehabilitation.

Development of wearable posture monitoring system for dynamic assessment of sitting posture

There have been increasing cases of people seeking treatment for neck and back pain. The most common cause of neck and back pain is due to long-term poor sitting posture. The most common poor sitting posture cases are humpback, and head and neck being too far forward. It is easy to cause neck and back pain and other symptoms. Therefore, the development of wearable posture monitoring system for dynamic assessment of sitting posture becomes both helpful and necessary. In addition to recording the wearer's posture when sitting with quantitative assessment, it is needed to execute real-time action feedback for correctness of posture, in order to reduce neck and back pain due to long-term poor sitting posture. This study completed an instant recording and dynamic assessment of position measurement and feedback system. The system consists of a number of dynamic measurement units that can describe the posture trajectory, which integrates three-axis gyro meter, three-axis accelerometer, and magnetometer in order to measure the dynamic tracking. In the reliability analysis experiment, angle measurement error is less than 2%. The correlation coefficient between correlation analysis and Motion Analysis (MA) is 0.97. It is shown that the motion trajectory of this system is highly correlated with MA. In the feasibility test of sitting position detection, it is possible to detect the sitting position from the basic action of the walking, standing, sitting and lying down, and the sensitivity reaches 95.84%. In the assessment of the sitting position, the information published by the Canadian Centre for Occupational Health and Safety was used, as well as the recommendations of professional physicians as a basis for evaluating the threshold of the sitting measurement parameters and immediately feedback to the subjects. The system developed in this study can be helpful to reduce neck and back pain due to long-term poor sitting posture.

Adjustable Method for Real-Time Gait Pattern Detection Based on Ground Reaction Forces Using Force Sensitive Resistors and Statistical Analysis of Constant False Alarm Rate

A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. To overcome these drawbacks, this study utilized the constant false alarm rate (CFAR) to analyze the characteristics of GCF signals. Specifically, a sliding window detector is built to record the lasting time of the curvature of the GCF signals and one complete gait cycle could be divided into three areas, such as continuous ascending area, continuous descending area and unstable area. Then, the GCF values in the unstable area are used to compute a threshold through the CFAR. Finally, the new gait pattern detection rules are proposed which include the results of the sliding window detector and the division results through the computed threshold. To verify this idea, a data acquisition board is designed to collect the GCF data from able-bodied subjects. Meanwhile, in order to test the reliability of the proposed method, five threshold-based methods in the literature are introduced as reference methods and the reliability is validated by comparing the detection results of the proposed method with those of the reference methods. Experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively and obtain high reliabilities compared with the reference methods.

Seven phases of gait detected in real-time using shank attached gyroscopes

A new gyroscope-based gait phase detection system (GPDS) with ability to detect all seven phases of gait was proposed in this study. Gyroscopes were attached to each shank. Shank angular velocity, about the medio-lateral axis, was streamed to a PC and a rule-based algorithm was used to identify characteristics of the signals. Five subjects were asked to walk on treadmill at their self-selected speed while using this system. All 7 phases of gait: LR, MSt, TSt, PSw, ISw, MSw, and TSw were detected in real-time using only shank angular velocities. To quantify system performance, sensor data was compared to simultaneously collected motion capture data. Average gait phase detection delays of the system were less than 40ms except TSw (74ms). The present system, consisting of minimal sensors and decreased processing, is precise, cosmetic, economical, and a good alternative for portable stand-alone applications.

Inertial Sensing for Gait Event Detection and Transfemoral Prosthesis Control Strategy

OBJECTIVE

This paper presents a method for walking gait event detection using a single inertial measurement unit (IMU) mounted on the shank.

METHODS

Experiments were conducted to detect heel strike (HS) and toe off (TO) gait events of 10 healthy subjects and 5 transfemoral amputees walking at various speeds and slopes on an instrumented treadmill. The performance of three different algorithms [thresholding (THR), linear discriminant analysis, and quadratic discriminant analysis] was evaluated on both timing and frequency of gait event detections compared to data collected using force plates.

RESULTS

Though all algorithms could be used reliably (within 8.2% stride temporal error and 0.2% frequency error), THR was the most accurate, detecting 100% of gait events within an average of 2% stride for both the healthy subjects and the amputees. Furthermore, universal parameters could be used across all speeds and slopes within each demographic.

CONCLUSION

HS and TO for walking gait can be reliably detected in healthy and transfemoral amputee subjects using a single IMU.

SIGNIFICANCE

This work provides a robust, simple, and inexpensive method of gait event detection that does not rely on a load cell and could be easily implemented in a lower-limb prosthesis.

Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

A Multicenter Trial of a Footdrop Stimulator Controlled by a Tilt Sensor

Objectives. To test the efficacy and acceptance of a footdrop stimulator controlled by a tilt sensor. Methods. A nonrandomized, test-retest study of 26 subjects with footdrop of more than 1 year’s duration, resulting from various central nervous system disorders, was performed in 4 centers for at least 3 months. Speed of walking in a straight line, speed around a figure of 8, and physiological cost index (PCI) were measured with and without the device. Hours/day and steps/day using the device were recorded. Results.All but 2 subjects used the tilt sensor at home, rather than a foot switch. Walking speed increased by 15% after 3 months ( n = 26; P < 0.01), 32% after 6 months ( n = 16; P < 0.01), and 47% after 12 months ( n = 8; P < 0.05), while PCI decreased. The number of steps taken per day of use increased significantly over time, and increased speed was directly correlated with usage. Walking speed also increased with the stimulator off, but to a lesser extent, indicating a training effect. Subject feedback from a questionnaire indicated satisfaction with the stimulator. Conclusions. Both efficacy and acceptance of the stimulator were good in a population of subjects with chronic footdrop.

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Objective assessment of postural stability in Parkinson's disease using mobile technology

BACKGROUND

A significant gap remains in the ability to effectively characterize postural instability in individuals with Parkinson's disease. Clinical evaluation of postural declines is largely subjective, whereas objective biomechanical approaches are expensive and time consuming, thus limiting clinical adoption. Recent advances in mobile devices present an opportunity to address the gap in the quantification of postural stability. The aim of this project was to determine whether kinematic data measured by hardware within a tablet device, a 3rd generation iPad, was of sufficient quantity and quality to characterize postural stability.

METHODS

Seventeen patients and 17 age-matched controls completed six balance conditions under altered surface, stance, and vision. Simultaneous kinematic measurements were gathered from a three-dimensional motion capture system and tablet.

RESULTS

The motion capture system and tablet provided similar measures of stability across groups. In particular, within the patient population, correlation between the two systems for peak-to-peak, normalized path length, root mean square, 95% volume, and total power values ranged from 0.66 to 1.00. Kinematic data from five balance conditions--double-leg stance with eyes open on a foam surface, double-leg stance with eyes closed on firm and foam surfaces, and tandem stance on firm and foam surfaces--were capable of discriminating patients from controls.

CONCLUSIONS

The hardware within the tablet provides data of sufficient accuracy for the quantification of postural stability in patients with Parkinson's disease. The objectivity, portability, and ease of use of this device make it ideal for use in clinical environments lacking sophisticated biomechanical systems.

Response of gait deficits to neuromuscular electrical stimulation for stroke survivors

Persistent gait deficits after stroke can cause falls, elevated energy cost and poor endurance. Coordination impairment is an underlying cause of gait deficits. Few efficacious interventions have been described that have targeted and measured restoration of coordinated gait components. Neuromuscular electrical stimulation can provide the critical gait practice characteristic of close-to-normal movements, by electrically inducing muscle contractions and coordinated movements that are not possible under volitional effort. Two-channel, surface neuromuscular electrical stimulation can be synchronized with phases of gait and can provide faster, more symmetrical neuromuscular electrical stimulation-assisted gait than gait with no neuromuscular electrical stimulation. Difficulties encountered during the use of surface neuromuscular electrical stimulation for gait training led to the development of neuromuscular electrical stimulation with implanted technologies. Implanted electrodes and/or stimulators proved to be feasible for gait training in stroke survivors. Gait training with a multichannel neuromuscular electrical stimulation system with implanted electrodes proved more advantageous than gait training without neuromuscular electrical stimulation, according to measures of volitional coordinated gait components (neuromuscular electrical stimulation deactivated).

Technological advances in interventions to enhance poststroke gait

Neurologic rehabilitation interventions may be either therapeutic or compensatory. Included in this article are lower extremity functional electrical stimulation, body weight-supported treadmill training, and lower extremity robotic-assisted gait training. These poststroke gait training therapies are predicated on activity-dependent neuroplasticity. All three interventions have been trialed extensively in research and clinical settings to show a positive effect on various gait parameters and measures of walking performance. This article provides an overview of evidence-based research that supports the efficacy of these three interventions to improve gait, as well as providing perspective on future developments to enhance poststroke gait in neurologic rehabilitation.

Proof of concept of a shoe based human activity monitor

This paper presents the proof of concept of a low power, low cost, wearable activity monitor. The functionality of the system is based on accurate stride detection from signals generated by two force sensing resistors integrated within a normal shoe. A novel algorithm is proposed that is able to differentiate between walking and non-walking activities with high accuracy. The performance of the proof of concept system was validated in five subjects who underwent five repetitions of three different speed walking tests, and five repetitions of five non-walking artefact generating tests. The system achieved a total sensitivity of 96% with 98% specificity and an overall accuracy of 94%.

Feasibility of using peroneal nerve recordings for deriving stimulation timing in a foot drop correction system

The objective of this research was to demonstrate the potential of using peroneal nerve activity to derive timing control for stimulation in foot drop correction and to attempt recording and stimulation through the same electrode. Two subjects were implanted with cuff electrodes on the peroneal nerve. An input domain was derived from the recorded electroneurogram (ENG) and fed to a detection algorithm based on an Adaptive Logic Network (ALN) for predicting stimulation timing. A switching circuit was furthermore built for switching between stimulator and recorder for combined use of the cuff electrode.  The detection was successful, but the accuracy depended on the signal to noise ratio of the recorded ENG. The switching circuit successfully allowed for simultaneous recording and stimulation through the same cuff electrode. We conclude that the peroneal nerve can potentially be used to record sensory information for derivation of a stimulator control signal in a foot drop application, while at the same time being stimulated to activate foot dorsiflexors.

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction

Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.

Command and control interfaces for advanced neuroprosthetic applications

Command and control interfaces permit the intention and situation of the user to influence the operation of the neural prosthesis. The wishes of the user are communicated via command interfaces to the neural prosthesis and the situation of the user by feedback control interfaces. Both these interfaces have been reviewed separately and are discussed in light of the current state of the art and projections for the future. It is apparent that as system functional complexity increases, the need for simpler command interfaces will increase. Such systems will demand more information to function effectively in order not to unreasonably increase user attention overhead. This will increase the need for bioelectric and biomechanical signals in a comprehensible form via elegant feedback control interfaces. Implementing such systems will also increase the computational demand on such neural prostheses.

External sensors for detecting the activation and deactivation times of the major muscles used in walking

Functional electrical stimulation (FES) can improve walking in individuals with mobility impairments. We evaluated accelerometers, force sensitive resistors, segment angles, and segment angular velocities to identify which sensor best determines the activation and deactivation times of the main muscles used during walking. This sensor(s) can be used in the future in conjunction with FES systems to improve walking. Able-bodied subjects walked at various speeds. Threshold levels were set for each sensor that minimized the difference between the times of activating and deactivating the electromyogram (EMG) of six muscles and the times of sensor threshold crossings as a percent of the step cycle. Mobility-impaired subjects walked at their preferred speed with and without FES to correct foot drop. Thresholds were set for these subjects so that sensor signals would cross at times that matched those of able-bodied subjects. Segment angles were generally the most effective sensor signals. Using segment angles of the thigh, shank, and foot, activation and deactivation times of the six muscles could be determined to within 6% of the step cycle. The shank segment angle produced the lowest overall error and was among the top three sensors for 10 of the 12 events (activation and deactivation of six muscle groups). A segment angle sensor was implemented using a complementary filter (accelerometer/gyroscope combination). Using this sensor improved rule-based timing of FES in subjects with foot drop as compared to accelerometers alone.

Gait analysis using wearable sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.

Automatic identification of gait events using an instrumented sock

Background

Textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait.

Methods

We investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal.

Results

Our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability.

Conclusions

This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.

Verification of accuracy and validity of gait phase detection system using motion sensors for applying walking assistive FES

In this study, we have analysed heel strike (HS) and toe off (TO) of normal individuals and hemiplegic patients, taking advantage of output curves acquired from various sensors, and verified the validity of sensor detection methods and their effectiveness when they were used for hemiplegic gaits. Gait phase detections using three different motion sensors were valid, since they all had reliabilities more than 95%, when compared with foot velocity algorithm. Results showed that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal individuals. Vertical acceleration could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient groups A and B. The detection of TO from all sensor signals was valid in both the patient groups A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B.

Methods for gait event detection and analysis in ambulatory systems

After stroke, hemiparesis is a common problem resulting in very individual needs for walking assistance. Often patients suffer from foot drop, i.e. inability to lift the foot from the ground during the swing phase of walking. Functional electrical stimulation is commonly used to correct foot drop. For all supporting stimulation devices, it is vital to adequately detect the gait events, which is traditionally obtained by a foot switch placed under the heel. To investigate present methods of gait analysis and detection for use in ambulatory rehabilitation systems, we carried out a meta-analysis on research studies. We found various sensors and sensor combinations capable of analyzing gait in ambulatory settings, ranging form simple force based binary switches to complex setups involving multiple inertial sensors and advanced algorithms. However additional effort is needed to minimize donning/doffing efforts, to overcome cosmetical aspects, and to implement those systems into closed loop ambulatory devices.

Gait event detection on level ground and incline walking using a rate gyroscope

Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. Accurate determination of the Initial Contact of the foot with the floor (IC) and the final contact or Foot Off (FO) on different terrains is important. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects walking outdoors on level ground, and up and down an incline. Performance was compared with a reference pressure measurement system. The mean difference between the gyroscope and the reference was less than −25 ms for IC and less than 75 ms for FO for all terrains. Detection success was over 98%. These results provide preliminary evidence supporting the use of the gyroscope for gait event detection on inclines as well as level walking.

Control of triceps surae stimulation based on shank orientation using a uniaxial gyroscope during gait

This article presents a stimulation control method using a uniaxial gyroscope measuring angular velocity of the shank in the sagittal plane, to control functional electrical stimulation of the triceps surae to improve push-off of stroke subjects during gait. The algorithm is triggered during each swing phase of gait when the angular velocity of the shank is relatively high. Subsequently, the start of the stance phase is detected by a change of sign of the gyroscope signal at approximately the same time as heel strike. Stimulation is triggered when the shank angle reaches a preset value since the beginning of stance. The change of angle is determined by integrating angular velocity from the moment of change of sign. The results show that the real-time reliability of stimulation control was at least 95% for four of the five stroke subjects tested, two of which were 100% reliable. For the remaining subject, the reliability was increased from 50% found during the experiment, to 99% during offline processing. Our conclusion is that a uniaxial gyroscope on the shank is a simple, more reliable alternative to the heel switch for the purpose of restoring push-off of stroke subjects during gait.

Motion analysis of sun salutation using magnetometer and accelerometer

Background:

Sun salutation is a part of yoga. It consists of a sequence of postures done with synchronized breathing. The practice of few cycles of sun salutation is known to help in maintaining good health and vigor. The practice of sun salutation does not need any extra gadgets. Also it is very much aerobic and invigorates the body and the mind. sun salutation, which comprises 10 postures, involves most of the joints of the body. Understanding the transition phase during motion is a challenging task, and thus, new convenient methods need to be employed.

Aims:

The purpose of this study was to get an insight into the motion analysis of sun salutation during the transition from each of the 10 postures.

Materials and Methods:

A device MicroStrain sensor 3DM-GX1, which is a combination of magnetometers, accelerometers, and gyroscopes was used to measure the inclination and the acceleration of the body along the three axes. The acceleration obtained was then separated into gravitational and kinematic components.

Results and Conclusions:

The value of the gravitational component helps us to understand the position of the body and the kinematic component helps us to analyze the grace of the motion.

Activity identification using body-mounted sensors--a review of classification techniques

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

A pelvic motion driven electrical stimulator for drop-foot treatment

Foot switches operating with force sensitive resistors placed in the shoe sole were considered as an effective way for driving FES assisted walking systems in gait restoration. However, the reliability and durability of the foot switches run down after a certain number of steps. As an alternative for foot switches, a simple, portable, and easy to handle motion driven electrical stimulator (ES) is provided for drop foot treatment. The device is equipped with a single tri-axis accelerometer worn on the pelvis, a commercial dual channel electrical stimulator, and a controller unit. By monitoring the pelvic rotation and acceleration during a walking cycle, the events including heel strike and toe off of each step is thereby predicted by a post-processing neural network model.

Functional electrical stimulation of walking: function, exercise and rehabilitation

For nearly half a century, functional electrical stimulation (FES) has been used to restore walking for people with paralysis and muscle weakness due to stroke and spinal cord injury. The first applications of the technology were intended to permanently replace lost neuromuscular function. Later, FES-assisted walking was found to have therapeutic benefits that include increased muscle strength, cardiovascular fitness and improved gait function that could be maintained after use of FES was terminated. In this review, we examine some of the major FES-assisted walking systems that have been developed for experimental and commercial purposes over the last four and a half decades, including foot drop stimulators, multichannel stimulators and hybrid orthotic systems.

Efficient FES triggering applying Kalman filter during sensory supported treadmill walking

In this paper an algorithm for a functional electrical stimulation (FES) gait re-education system for incomplete spinal cord injured persons, providing efficient stimulation triggering, is presented. During neurological impaired gait FES was provided as motor augmentation support. Simultaneously the gait kinematics were recorded using the proposed sensory system, which is equipped with a dual-axial accelerometer and a gyroscope. The sensory device was placed at the shank of the paretic leg. The data assessed were input into a mathematical algorithm applied for shank angle estimation. The algorithm is based on the Kalman filter, estimating the angle error and correcting the actual measurement. Furthermore the information was combined with other kinematic data for the purpose of efficient and reliable stimulation triggering. The algorithm was tested with preliminary measurements on several neurologically intact persons during even terrain and treadmill walking. Trial measurements were verified with a contactless optical measurement system, with FES only simulated on controller output. Later on a treadmill training in combination with FES triggering was carried out. The outcome of the measurements shows that the use of sensory integration may successfully solve the problem of data assessment in dynamic movement where an inclinometer does not provide sufficient information for efficient control of FES.

Clinical application of acceleration sensor to detect the swing phase of stroke gait in functional electrical stimulation

Functional electrical stimulation (FES) can improve the gait of stroke patients by stimulating the peroneal nerve in the swing phase of the affected leg, causing dorsiflexion of the foot that allows the toes to clear the ground. A sensor can trigger the electrical stimulation automatically during the stroke gait. We previously used a heel sensor system, which detects the contact pressure of the heel, in FES to correct foot drop gait. However, the heel sensor has disadvantages in cosmetics and durability. Therefore, we have replaced the heel sensor with an acceleration sensor that can detect the swing phase based on the acceleration speed of the affected leg, using a machine learning technique (Neural Network). We have used a signal for heel contact in a gait using the heel sensor before training with the Neural Network. The accuracy of the Neural Network detector was compared with a swing phase detector based on the heel sensor. The Neural Network detector was able to detect similarly the swing phase in the heel sensor. The largest difference in timing of the swing phase was less than 60 milliseconds in normal subjects and 80 milliseconds in stroke patients. We were able to correct foot drop gait using FES with an acceleration sensor and Neural Network detector. The present results indicate that an acceleration sensor positioned on the thigh, which is cosmetically preferable to systems in which the sensor is farther from the entry point of the electrodes, is useful for correction of stroke gait using FES.

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

Identification of gait events using an optimal sensor set and a reliable algorithm would be useful in the clinical evaluation of patients with dropped foot. This article describes a threshold detection method for identifying gait events and evaluating the reliability of a system on ten subjects with dropped foot and three non-impaired controls. The system comprised three sensor units of accelerometers and gyroscopes attached at the thigh, shank and foot of the impaired leg in subjects with dropped foot, and the dominant leg in the controls. A performance index was devised to compare the values of different measuring directions of the sensor units and evaluate the system's reliability. The performance index, with the ideal value equal to 1, depended on the classification accuracy and timing variation of the turning points. These were obtained from the threshold detection method that distinguished the absolute maximum and minimum turning points from local maximum and minimum turning points. It was found that some specific turning points could effectively identify gait events with a high median value in the performance index. These turning points included: the minimum turning point in superior-inferior acceleration on the thigh at loading response (0.972); the minimum turning point in anterior-posterior angular velocity on the shank at pre-swing (0.955) and the maximum turning point in superior-inferior acceleration on the foot at initial swing (0.954). Combining the results of sensor measurements in different orientations and attachment locations could be used for gait event identification. It was shown that the threshold detection method is reliable. Portable gait-monitoring devices can be used for monitoring of daily activities and functional control.

A new means of transcutaneous coupling for neural prostheses

Neural prostheses are electronic stimulators that activate nerves to restore sensory or motor functions. Implanted neural prostheses receive command signals and in some cases energy to recharge their batteries through the skin by telemetry. Here, we describe a new approach that eliminates the implanted stimulator. Stimulus pulse trains are passed between two surface electrodes placed on the skin. An insulated lead with conductive terminals at each end is implanted inside the body. One terminal is located under the cathodal surface electrode and the other is attached to a nerve targeted for stimulation. A fraction (10%-15%) of the current flowing between the surface electrodes is routed through the implanted lead. The nerve is stimulated when the amount of routed current is sufficient. The aims of this study were to establish some basic electrical properties of the system and test long-term stability in chronic implants. Stimulation of the nerve innervating the ankle flexors produced graded force over the full physiological range at amplitudes below threshold for evoking muscle contractions under the surface electrodes. Implants remained stable for over 8 mo. The findings provide the basis for a new family of neural prostheses.

A wearable pelvic sensor design for drop foot treatment in post-stroke patients

A novel wearable pelvic sensor design for gait analysis was developed and evaluated in both normal and pathological gait. The device is a hip worn fusion of gyroscopes and accelerometers which allows for monitoring of the periodic vertical rotation of the pelvis during the walking cycle and uses this information as a predictor of gait events such as Heel-Strike (HS) and Toe-Off (TO). The gait pattern of two age and gender-matched groups (40-65 years) of 10 healthy subjects (5 male, 5 female) and 10 subjects with hemiplegic drop foot were examined. The pelvic sensor method was correlated against an optical motion system and footswitches, to evaluate the technique's efficacy at detecting foot contact events in walking and hip pattern. Data analysis showed the device was able to predict foot contact events from recorded maximum and minimum pelvic angle (TO: Healthy - 130ms Hemiplegic - 95ms; HS: Healthy - 127ms, Hemiplegic - 96ms). This ability to detect gait events would allow this sensor design to be used in simplifying drop foot stimulation systems. The proposed method also records the relative range of motion of the pelvis from which useful information on gait symmetry can be obtained and used in ambulatory monitoring or treatment intervention analysis.

Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons

Functional electrical stimulation (FES) holds great potential for restoring motor functions after brain and spinal cord injury. Currently, most FES systems are under simple finite state control, using external sensors which tend to be bulky, uncomfortable and prone to failure. Sensory nerve signals offer an interesting alternative, with the possibility of continuous feedback control. To test feasibility, we recorded from ensembles of sensory neurons with microelectrode arrays implanted in the dorsal root ganglion (DRG) of walking cats. Limb position and velocity variables were estimated accurately (average R2 values >0.5) over a range of walking speeds (0.1-0.5 m s(-1)) using a linear combination of firing rates from 10 or more neurons. We tested the feasibility of sensory control of intraspinal FES by recording from DRG neurons during hindlimb movements evoked by intraspinal microstimulation of the lumbar spinal cord in an anesthetized cat. Although electrical stimulation generated artifacts, this problem was overcome by detecting and eliminating events that occurred synchronously across the array of microelectrodes. The sensory responses to limb movement could then be measured and decoded to generate an accurate estimate of the limb state. Multichannel afferent recordings may thus provide FES systems with the feedback needed for adaptive control and perturbation compensation, though long-term stability remains a challenge.

BIONic WalkAide for correcting foot drop

The goal of this study was to test the feasibility and efficacy of using microstimulators (BIONs) to correct foot drop, the first human application of BIONs in functional electrical stimulation (FES). A prototype BIONic foot drop stimulator was developed by modifying a WalkAide2 stimulator to control BION stimulation of the ankle dorsiflexor muscles. BION stimulation was compared with surface stimulation of the common peroneal nerve provided by a normal WalkAide2 foot drop stimulator. Compared to surface stimulation, we found that BION stimulation of the deep peroneal nerve produces a more balanced ankle flexion movement without everting the foot. A 3-D motion analysis was performed to measure the ankle and foot kinematics with and without stimulation. Without stimulation, the toe on the affected leg drags across the ground. The BIONic WalkAide elevates the foot such that the toe clears the ground by 3 cm, which is equivalent to the toe clearance in the unaffected leg. The physiological cost index (PCI) was used to measure effort during walking. The PCI is high without stimulation (2.29 +/- 0.37; mean +/- S.D.) and greatly reduced with surface (1.29 +/- 0.10) and BION stimulation (1.46 +/- 0.24). Also, walking speed is increased from 9.4 +/- 0.4 m/min. without stimulation to 19.6 +/- 2.0 m/min. with surface and 17.8 +/- 0.7 m/min. with BION stimulation. We conclude that functional electrical stimulation with BIONs is a practical alternative to surface stimulation and provides more selective control of muscle activation.

Physiologically based controller for generating overground locomotion using functional electrical stimulation

The physiological control of stepping is governed both by signals descending from supraspinal systems and by circuitry residing within the lumbosacral spinal cord. The goal of this study was to evaluate the capacity of physiologically based controllers to restore functional overground locomotion after neurological damage, such as spinal cord injury when used in conjunction with functional electrical stimulation. For this purpose we implemented and tested two controllers: 1) an intrinsically timed system that generated a predetermined rhythmic output and 2) a sensory-based system that used feedback signals to make appropriate transitions between the unloaded (flexion) and loaded (extension) phases of the gait cycle. A third controller, a combination of the intrinsically timed and sensory-driven controllers, was implemented and two sessions were conducted to demonstrate the functional advantages of this approach. The controllers were tested in anesthetized cats, implanted with intramuscular electrodes in six major extensor and flexor muscles of the hindlimbs. The cats were partially supported on a sliding trolley that was propelled by the hindlimbs along a 2.5-m instrumented walkway. Ground reaction forces and limb positions were measured by force plates in the walkway and by accelerometers secured to the legs of the cat, respectively. The controllers were used to generate patterns of stimulation that would elicit alternating flexor (swing) and extensor (stance) movements in the hindlimbs. Using either the intrinsically timed or sensory-driven controllers, the cats were able to travel a distance of 2.5 m, taking five to 12 steps. Functional stepping sequences were more easily achieved using the intrinsically timed controller as the result of a lower sensitivity to the selection of initial stimulation parameters. However, unlike the sensory-driven controller, the intrinsically timed controller was unable to adjust to overcome walkway resistance and muscle fatigue. Neither system was consistently able to ensure load-bearing stepping. Therefore we propose the use of a "combined controller" that relies heavily on intrinsic timing but that can be reset based on sensory signals. A combined controller such as this one may provide the best solution for restoring robust overground locomotion after spinal cord injury.

Neuromuscular electrical stimulation in neurorehabilitation

This review provides a comprehensive overview of the clinical uses of neuromuscular electrical stimulation (NMES) for functional and therapeutic applications in subjects with spinal cord injury or stroke. Functional applications refer to the use of NMES to activate paralyzed muscles in precise sequence and magnitude to directly accomplish functional tasks. In therapeutic applications, NMES may lead to a specific effect that enhances function, but does not directly provide function. The specific neuroprosthetic or "functional" applications reviewed in this article include upper- and lower-limb motor movement for self-care tasks and mobility, respectively, bladder function, and respiratory control. Specific therapeutic applications include motor relearning, reduction of hemiplegic shoulder pain, muscle strengthening, prevention of muscle atrophy, prophylaxis of deep venous thrombosis, improvement of tissue oxygenation and peripheral hemodynamic functioning, and cardiopulmonary conditioning. Perspectives on future developments and clinical applications of NMES are presented.

Use of gyroscopic sensors for objective evaluation of trimming and shoeing to alter time between heel and toe lift-off at end of the stance phase in horses walking and trotting on a treadmill

OBJECTIVE

To determine whether a shoe with an axialcontoured lateral branch would induce greater lateral roll of the forelimb hoof during the time between heel and toe lift-off at end of the stance phase (breakover). Animals-10 adult horses.

PROCEDURE

A gyroscopic transducer was placed on the hoof of the right forelimb and connected to a transmitter. Data on hoof angular velocity were collected as each horse walked and trotted on a treadmill before (treatment 1, no trim-no shoe) and after 2 treatments by a farrier (treatment 2, trim-standard shoe; and treatment 3, trim-contoured shoe). Data were converted to hoof angles by mathematical integration. Breakover duration was divided into 4 segments, and hoof angles in 3 planes (pitch, roll, and yaw) were calculated at the end of each segment. Multivariable ANOVA was performed to detect differences among treatments and gaits.

RESULTS

Trimming and shoeing with a shoe with contoured lateral branches induced greater mean lateral roll to the hoof of 3.2 degrees and 2.5 degrees during the first half of breakover when trotting, compared with values for no trim-no shoe and trim-standard shoe, respectively. This effect dissipated during the second half of breakover. When horses walked, lateral roll during breakover was not significantly enhanced by use of this shoe.

CONCLUSIONS AND CLINICAL RELEVANCE

A shoe with an axial-contoured lateral branch induced greater lateral roll during breakover in trotting horses, but change in orientation of the hoof was small and limited to the first half of breakover.

Reanimating limbs after injury or disease

Many diseases and injuries lead to loss of motor function. Can we reanimate paralyzed limbs to produce effective, graceful movements? Recent insights into the function of the motor system and greatly improved computing capabilities have made this a realistic goal, even in the absence of regeneration of motor pathways. Some approaches involve stimulating muscles, nerves or the spinal cord below the level of a lesion. Others involve recording a subject's intention from the cortex, and using this intention to control computers, robots or systems for stimulating the limbs. Here, we critically analyze the possibilities and limitations of various approaches for restoring motor function based on recent human trials and underlying neuroscience research.