Feasibility of Magnetic Resonance-Compatible Accelerometers to Monitor Tremor Fluctuations During Magnetic Resonance-Guided Focused Ultrasound Thalamotomy: Technical Note

BACKGROUND

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is used to treat essential tremor and tremor-dominant Parkinson disease. Feedback is collected throughout the procedure to verify the location of the target and completeness of response; however, variability in clinical judgments may underestimate or overestimate treatment response.

OBJECTIVE

To objectively quantify joint motion after each sonication using accelerometers secured to the contralateral upper extremity in an effort to optimize MRgFUS treatment.

METHODS

Before the procedure, 3 accelerometers were secured to the patient's arm, forearm, and index finger. Throughout the procedure, tremor motion was regularly recorded during postural and kinetic tremor testing and individual joint angle measures were modeled. The joint angle from each accelerometer was compared with baseline measurements to assess changes in angles. Subsequent adjustments to the target location and sonication energy were made at the discretion of the neurosurgeon and neuroradiologist.

RESULTS

Intraoperative accelerometer measurements of hand tremor from 18 patients provided quantified data regarding joint angle reduction: 87.3%, 94.2%, and 86.7% for signature writing, spiral drawing, and line drawing tests, respectively. Target adjustment based on accelerometer monitoring of the angle at each joint added substantial value toward achieving optimal tremor reduction.

CONCLUSION

Real-time accelerometer recordings collected during MRgFUS thalamotomy offered objective quantification of changes in joint angle after each sonication, and these findings were consistent with clinical judgments of tremor response. These results suggest that this technique could be used for fine adjustment of the location of sonication energy and number of sonications to consistently achieve optimal tremor reduction.

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion's translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This review article will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. We will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults.

Parametric evaluation of deep brain stimulation parameter configurations for Parkinson's disease using a conformal wearable and wireless inertial sensor system and machine learning

Deep brain stimulation enables highly specified patient-unique therapeutic intervention ameliorating the symptoms of Parkinson's disease. Inherent to the efficacy of deep brain stimulation is the acquisition of an optimal parameter configuration. Using conventional methods, the optimization process for tuning the deep brain stimulation system parameters can intrinsically induce strain on clinical resources. An advanced means of quantifying Parkinson's hand tremor and distinguishing between parameter settings would be highly beneficial. The conformal wearable and wireless inertial sensor system, such as the BioStamp nPoint, has a volumetric profile on the order of a bandage that readily enables convenient quantification of Parkinson's disease hand tremor. Furthermore, the BioStamp nPoint has been certified by the FDA as a 510(k) medical device for acquisition of medical grade data. Parametric variation of the amplitude parameter for deep brain stimulation can be quantified through the BioStamp nPoint conformal wearable and wireless inertial sensor system mounted to the dorsum of the hand. The acquired inertial sensor signal data can be wirelessly transmitted to a secure Cloud computing environment for post-processing. The quantified inertial sensor data for the parametric study of the effects of varying amplitude can be distinguished through machine learning classification. Software automation through Python can consolidate the inertial sensor data into a suitable feature set format. Using the multilayer perceptron neural network considerable machine learning classification accuracy is attained to distinguish multiple parametric settings of amplitude for deep brain stimulation, such as 4.0 mA, 2.5 mA, 1.0 mA, and 'Off' status representing a baseline. These findings constitute an advance toward the pathway of attaining real-time closed loop automated parameter configuration tuning for treatment of Parkinson's disease using deep brain stimulation.

Kinematic Validation of Postural Sway Measured by Biodex Biosway (Force Plate) and SWAY Balance (Accelerometer) Technology

Background

The Biodex Biosway® Balance System and SWAY Balance® Mobile smartphone application (SBMA) are portable instruments that assess balance function with force plate and accelerometer technology, respectively. The validity of these indirect clinical measures of postural sway merits investigation.

Purpose

The purpose of this study was to investigate the concurrent validity of standing postural sway measurements by using the portable Biosway and SBMA systems with kinematic measurements of the whole body Center of Mass (COM) derived from a gold-standard reference, a motion capture system.

Study Design

Cross-sectional; repeated measures.

Methods

Forty healthy young adults (21 female, 19 male) participated in this study. Participants performed 10 standing balance tasks that included combinations of standing on one or two legs, with eyes open or closed, on a firm surface or foam surface and voluntary rhythmic sway. Postural sway was measured simultaneously from SBMA, Biosway, and the motion capture system. The linear relationships between the measurements were analyzed.

Results

Significant correlations were found between Biosway and COM velocity for both progressively challenging single and double leg stances (τ_b = 0.3 to 0.5, p < 0.01 to <0.0001). SBMA scores and COM velocity were significantly correlated only for single leg stances (τ_b = −0.5 to −0.6, p < 0.0001). SBMA scores had near-maximal values with zero to near-zero variance in double leg stances, indicating a ceiling effect.

Conclusion

The force plate-based Biodex Biosway is valid for assessing standing postural sway for a wide range of test conditions and challenges to standing balance, whereas an accelerometer-based SWAY Balance smartphone application is valid for assessing postural sway in progressively challenging single leg stance but is not sensitive enough to detect lower-magnitude postural sway changes in progressively challenging double leg stances.

Design of Instrumented Shoes for Gait Characterization: A Usability Study With Healthy and Post-stroke Hemiplegic Individuals

Ambulation is a fundamental requirement of human beings for enjoying healthy community life. A neurological disorder such as stroke can significantly affect one's gait thereby restricting one's active community participation. To quantify one's gait, spatiotemporal gait parameters are widely used in clinical context with different tests such as 10 meter walk test, 6 min walk test, etc. Though these conventional observation-based methods are powerful, yet they often suffer from subjectivity, a scarcity of adequately trained therapists and frequent clinical visits for assessment. Researchers have been exploring the technology-assisted solutions for gait characterization. There are laboratory-based stereophotogrammetric methods and walk mats that are powerful tools as far as gait characterization is concerned. However, these suffer from issues with portability, accessibility due to high cost, labor-intensiveness, etc. Faced with these issues, our present research tries to investigate and quantify the gait abnormalities in individuals with neurological disorder by using a portable and cost-effective instrumented shoes (Shoes_FSRhenceforth). The in-house developed Shoes_FSR comprised of a pair of shoes instrumented with Force Sensing Resistors (FSR) and a wireless data acquisition unit. The real-time FSR data was acquired wirelessly and analyzed by a central console to offer quantitative indices of one's gait. Studies were conducted with 15 healthy participants and 9 post-stroke survivors. The spatiotemporal gait parameters of healthy participants measured using Shoes_FSR were validated with standard methods such as stereophotogrammetric system and paper-based setup. Statistical analysis showed good agreement between the gait parameters measured using Shoes_FSR and the standard methods. Specifically, the mean absolute error of the spatial parameters measured by the Shoes_FSR, in the worst case, was 1.24% and that for the temporal parameters was 1.12% with that measured by standard methods for healthy gait. This research shows the potential of the Shoes_FSR to quantify gait abnormality of post-stroke hemiplegic patients. In turn, the results show a promise for the future clinical use of the Shoes_FSR.

Accelerometer-based predictive models of fall risk in older women: a pilot study

Current clinical methods of screening older adults for fall risk have difficulties. We analyzed data on 67 women (mean age = 77.5 years) who participated in the Objective Physical Activity and Cardiovascular Health (OPACH) study within the Women’s Health Initiative and in an accelerometer calibration substudy. Participants completed the short physical performance battery (SPPB), questions about falls in the past year, and a timed 400-m walk while wearing a hip triaxial accelerometer (30 Hz). Women with SPPB ≤ 9 and 1+reported falls (n = 19) were grouped as high fall risk; women with SPPB = 10–12 and 0 reported falls (n = 48) were grouped as low fall risk. Random Forests were trained to classify women into these groups, based upon traditional measures of gait and/or signal-based features extracted from accelerometer data. Eleven models investigated combined feature effects on classification accuracy, using 10-fold cross-validation. The models had an average 73.7% accuracy, 81.1% precision, and 0.706 AUC. The best performing model including triaxial data, cross-correlations, and traditional measures of gait had 78.9% accuracy, 84.4% precision, and 0.846 AUC. Mediolateral signal-based measures—coefficient of variance, cross-correlation with anteroposterior accelerations, and mean acceleration—ranked as the top 3 features. The classification accuracy is promising, given research on probabilistic models of falls indicates accuracies ≥80% are challenging to achieve. The results suggest accelerometer-based measures captured during walking are potentially useful in screening older women for fall risk. We are applying algorithms developed in this paper on an OPACH dataset of 5000 women with a 1-year prospective falls log and week-long, free-living accelerometer data.

A hip-worn device that measures walking motion can help identify which older women are at heightened risk for falling. Andrew Hua, from the University of Illinois at Urbana-Champaign, USA, and colleagues put 67 elderly women through a series of tests to assess their lower extremity function. They also asked the study participants about fall histories in the past year and strapped a triaxial accelerometer to the women’s hips while they completed a 400-meter walking test. Analyses showed that the accelerometry data, when fed into a machine-learning algorithm, were predictive of physical ability and fall risk. Based on these results, the researchers are validating the algorithm in a larger study of 5000 women who wore hip accelerometers for a full week and reported falls prospectively for one-year.

Clinical Gait Evaluation of Patients with Lumbar Spine Stenosis

OBJECTIVE

The third generation Intelligent Device for Energy Expenditure and Activity (IDEEA3, MiniSun, CA) has been developed for clinical gait evaluation, and this study was designed to evaluate the accuracy and reliability of IDEEA3 for the gait measurement of lumbar spinal stenosis (LSS) patients.

METHODS

Twelve healthy volunteers were recruited to compare gait cycle, cadence, step length, velocity, and number of steps between a motion analysis system and a high-speed video camera. Twenty hospitalized LSS patients were recruited for the comparison of the five parameters between the IDEEA3 and GoPro camera. Paired t-test, intraclass correlation coefficient, concordance correlation coefficient, and Bland-Altman plots were used for the data analysis.

RESULTS

The ratios of GoPro camera results to motion analysis system results, and the ratios of IDEEA3 results to GoPro camera results were all around 1.00. All P-values of paired t-tests for gait cycle, cadence, step length, and velocity were greater than 0.05, while all the ICC and CCC results were above 0.950 with P < 0.001.

CONCLUSIONS

The measurements for gait cycle, cadence, step length, velocity, and number of steps with the GoPro camera are highly consistent with the measurements with the motion analysis system. The measurements for IDEEA3 are consistent with those for the GoPro camera. IDEEA3 can be effectively used in the gait measurement of LSS patients.

Population measurement for health systems

How can health systems make good use of digital medicine? For healthcare infrastructure, the answer is population measurement, monitoring people to compute status for clustering cohorts. In chronic care, most effective is measuring all the time, to track health status as it gradually changes. Passive monitors run in the background, without additional tasks to activate monitors, especially on mobile phones. At its core, a health system is a "sorting problem". Each patient entering the system must be effectively sorted into treatment cohorts. Health systems have three primary problems: Case Finding (which persons have which diagnoses), Risk Stratification (which persons are which status), and Care Routing (which persons need which treatments). The issue is then which measures can be continuously monitored at appropriate periodicity. The solutions of population measurement measure vital signs with passive monitors. These are input to predictive analytics to detect clinical values for providing care within health systems. For chronic care, complex vitals must be measured for overall status, such as oxygen saturation or gait speed. This enables healthcare infrastructure to support stratification, with persons placed into current levels of health status. Practical considerations for health systems influence implementation of new infrastructure. Case finding is more likely to be useful in urban settings, with barriers to entry based upon lower incomes. Care routing is more likely to be useful in rural settings, with barriers to entry based upon isolated geographies. Viable healthcare at acceptable quality and affordable cost is now possible for the range of geographies and incomes.

Clinical gait evaluation of patients with knee osteoarthritis

Knee osteoarthritis (KOA) is the most common osteoarthritis in lower limbs, and gait measurement is important to evaluate walking function of KOA patients before and after treatment. The third generation Intelligent Device for Energy Expenditure and Activity (IDEEA3) is a portable gait analysis system to evaluate gaits. This study is to evaluate the accuracy and reliability of IDEEA3 for gait measurement of KOA patients. Meanwhile, gait differences between KOA patients and healthy subjects are examined. Twelve healthy volunteers were recruited for measurement comparison of gait cycle (GC), cadence, step length, velocity and step counts between a motion analysis system and a high-speed camera (GoPro Hero3). Twenty-three KOA patients were recruited for measurement comparison of former five parameters between GoPro Hero3 and IDEEA3. Paired t-test, Concordance Correlation Coefficient (CCC) and Intraclass Correlation Coefficient (ICC) were used for data analysis. All p-values of paired t-tests for GC, cadence, step length and velocity were greater than 0.05 while all CCC and ICC results were above 0.95. The measurements of GC, cadence, step length, velocity and step counts by motion analysis system are highly consistent with the measurements by GoPro Hero3. The measurements of former parameters by GoPro Hero3 are not statistically different from the measurements by IDEEA3. IDEEA3 can be effectively used for the measurement of GC, cadence, step length, velocity and step counts in KOA patients. The KOA patients walk with longer GC, lower cadence, shorter step length and slower speed compared with healthy subjects in natural speed with flat shoes.

Wireless gyroscope platform enabled by a portable media device for quantifying wobble board therapy

The wobble board enables a therapy strategy for rehabilitation of the ankle foot complex. Quantification of therapy, such as through the use of a wobble board, can facilitate a therapist's acuity for advancing and optimizing the overall therapy strategy. The portable media device, such as an iPod, can be equipped with a software application to function as a wireless gyroscope platform. Integration of the wobble board with the portable media device functioning as a wireless gyroscope enables the potential for patient to therapist interaction through connectivity to the Internet. A patient can conduct wobble board therapy for the ankle foot complex from the convenient vantage point of a homebound setting with therapy data transmitted wirelessly as email attachments. The gyroscope signal of the wobble board therapy can be consolidated into a feature set for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy has been achieved for differentiating between a hemiplegic affected ankle and unaffected ankle while using a wobble board. The combination of machine learning, wireless systems, such as a portable media device functioning as a wireless gyroscope, and a conventional therapy device, such as a wobble board, are envisioned to advance the capability to optimally impact the rehabilitation experience.

Smartphone-based evaluation of parkinsonian hand tremor: quantitative measurements vs clinical assessment scores

With an ever-growing number of technologically advanced methods for the diagnosis and quantification of movement disorders, comes the need to assess their accuracy and see how they match up with widely used standard clinical assessment tools. This work compares quantitative measurements of hand tremor in twenty-three Parkinson's disease patients, with their clinical scores in the hand tremor components of the Unified Parkinson's Disease Rating Scale (UPDRS), which is considered the "gold standard" in the clinical assessment of the disease. Our measurements were obtained using a smartphone-based platform, which processes the phone's accelerometer and gyroscope signals to detect and measure hand tremor. The signal metrics used were mainly based on the magnitude of the acceleration and the rotation rate vectors of the device. Our results suggest relatively strong correlation (r>0.7 and p<;0.01) between the patients' UPDRS hand tremor scores and the signal metrics applied to the measured signals.

Actigraphy features for predicting mobility disability in older adults

Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mobility impairment and major mobility disability (MMD). Men (N  =  357) and women (N  =  778) aged 70-89 years wore a tri-axial accelerometer (Actigraph GT3X) on the right hip during free-living conditions for 8.4  ±  3.0 d. One-second epoch data were summarized into 67 features. Several machine learning techniques were used to select features from the free-living condition to predict mobility impairment, defined as 400 m walking speed  <0.80 m s^-1. Selected features were also included in a model to predict the first occurrence of MMD-defined as the loss in the ability to walk 400 m. Each method yielded a similar estimate of 400 m walking speed with a root mean square error of ~0.07 m s^-1 and an R-squared values ranging from 0.37-0.41. Sensitivity and specificity of identifying slow walkers was approximately 70% and 80% for all methods, respectively. The top five features, which were related to movement pace and amount (activity counts and steps), length in activity engagement (bout length), accumulation patterns of activity, and movement variability significantly improved the prediction of MMD beyond that found with common covariates (age, diseases, anthropometry, etc). This study identified a subset of actigraphy features collected in free-living conditions that are moderately accurate in identifying persons with clinically-assessed mobility impaired and significantly improve the prediction of MMD. These findings suggest that the combination of features as opposed to a specific feature is important to consider when choosing features and/or combinations of features for prediction of mobility phenotypes in older adults.

EFFECT OF AGE, GENDER, AND WALKWAY LENGTH ON ACCELEROMETRY-BASED GAIT PARAMETERS FOR HEALTHY ADULT SUBJECTS

Accelerometry is now a well-established method for monitoring human body movements, and is increasingly being used for gait analysis under nonlaboratory conditions because of its low-cost and unobtrusive nature. In order to encourage its use in the clinical setting such as for assessing functional declines due to aging or disease, an extensive database of healthy gait is needed. This paper presents reference data for 245 normal Japanese adults (126 men and 119 women aged 40–86 years) obtained from indoor walk tests by using a trunk-mounted acceleration sensor. Seven gait parameters were extracted from the acceleration data measured at fast, normal, and slow gait for 5[Formula: see text]m and 10[Formula: see text]m walkways. The effects of age on cadence, speed, and step length were consistent with those observed in previous studies. Scaled speed and acceleration were closely correlated with each other, and exhibited similar gender- and age-associated behavior, indicating that they could be used interchangeably in gait analysis. A comparison of these parameters between different walkways revealed a significant effect of walkway length. Our parameters may provide a useful reference database for the clinical analysis of not only healthy gait but also impaired gait for the 10[Formula: see text]m walkway as well as for the shorter 5[Formula: see text]m walkway.

A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson's Disease

Background

A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs.

Methods

The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously.

Results

Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes.

Conclusion

These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.

Recognizing the intensity of strength training exercises with wearable sensors

In this paper we propose a system based on a network of wearable accelerometers and an off-the-shelf smartphone to recognize the intensity of stationary activities, such as strength training exercises. The system uses a hierarchical algorithm, consisting of two layers of Support Vector Machines (SVMs), to first recognize the type of exercise being performed, followed by recognition of exercise intensity. The first layer uses a single SVM to recognize the type of the performed exercise. Based on the recognized type a corresponding intensity prediction SVM is selected on the second layer, specializing in intensity prediction for the recognized type of exercise. We evaluate the system for a set of upper-body exercises using different weight loads. Additionally, we compare the most important features for exercise and intensity recognition tasks and investigate how different sliding window combinations, sensor configurations and number of training subjects impact the algorithm performance. We perform all of the experiments for two different types of features to evaluate the feasibility of implementation on resource constrained hardware. The results show the algorithm is able to recognize exercise types with approximately 85% accuracy and 6% intensity prediction error. Furthermore, due to similar performance using different types of features, the algorithm offers potential for implementation on resource constrained hardware.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Using a foot mounted accelerometer to detect changes in gait patterns

The purpose of this study is to investigate how data from a foot mounted accelerometer can be used to detect motor pattern healthy subjects performed walking trails under two different conditions; normal and stiff ankle walking. Lower body kinematic data were collected as well as accelerometer data from both feet. An algorithm is presented which quantifies relevant swing phase characteristics from the foot accelerometer. Peak total acceleration during initial swing was significantly higher in the stiff ankle condition (M = 33.10, SD = 5.12) than in the normal walking condition (M = 29.47, SD = 5.75; t(7) = 4.32, p = .003, two-tailed). There was a large effect size (eta squared = 0.853). Time between peak acceleration during initial swing to foot strike was significantly shorter in the stiff ankle condition (M = 0.42, SD = 0.02) than in the normal condition (M = 0.44, SD = 0.03; t(7) = -2.54, p = .039, two- tailed). There was a large effect size (eta squared = 0.693). Simple to process metrics from tri-axial accelerometer data on the foot show potential to detect changes in ankle kinematic patterns.

Effects of kinesthetic haptic feedback on standing stability of young healthy subjects and stroke patients

Background

Haptic control is a useful therapeutic option in rehabilitation featuring virtual reality interaction. As with visual and vibrotactile biofeedback, kinesthetic haptic feedback may assist in postural control, and can achieve balance control. Kinesthetic haptic feedback in terms of body sway can be delivered via a commercially available haptic device and can enhance the balance stability of both young healthy subjects and stroke patients.

Method

Our system features a waist-attached smartphone, software running on a computer (PC), and a dedicated Phantom Omni® device. Young healthy participants performed balance tasks after assumption of each of four distinct postures for 30 s (one foot on the ground; the Tandem Romberg stance; one foot on foam; and the Tandem Romberg stance on foam) with eyes closed. Patient eyes were not closed and assumption of the Romberg stance (only) was tested during a balance task 25 s in duration. An Android application running continuously on the smartphone sent mediolateral (ML) and anteroposterior (AP) tilt angles to a PC, which generated kinesthetic haptic feedback via Phantom Omni®. A total of 16 subjects, 8 of whom were young healthy and 8 of whom had suffered stroke, participated in the study.

Results

Post-experiment data analysis was performed using MATLAB®. Mean Velocity Displacement (MVD), Planar Deviation (PD), Mediolateral Trajectory (MLT) and Anteroposterior Trajectory (APT) parameters were analyzed to measure reduction in body sway. Our kinesthetic haptic feedback system was effective to reduce postural sway in young healthy subjects regardless of posture and the condition of the substrate (the ground) and to improve MVD and PD in stroke patients who assumed the Romberg stance. Analysis of Variance (ANOVA) revealed that kinesthetic haptic feedback significantly reduced body sway in both categories of subjects.

Conclusion

Kinesthetic haptic feedback can be implemented using a commercial haptic device and a smartphone. Intuitive balance cues were created using the handle of a haptic device, rendering the approach very simple yet efficient in practice. This novel form of biofeedback will be a useful rehabilitation tool improving the balance of stroke patients.

FOOT LOADING PATTERNS WITH DIFFERENT UNSTABLE SOLES STRUCTURE

Foot loading patterns can be changed by using different unstable sole structures, detailed quantification of which is of great significance for research and technological development in falling prevention and lower limb disorders rehabilitation. In this study, unstable soles constructions are adjusted through unstable elements in heel and medial, neutral and lateral forefoot and the foot loading patterns are comparatively studied. A total of 22 healthy male subjects participated in this test. Subjects are asked to walk over a 12 m walkway with control shoes and experimental shoes in self-adapted speed. Significant peak pressure, contact area and pressure-time integral differences in middle foot are found between control shoes and experimental shoes. In addition, peak pressure and pressure-time integral are found to increase significantly with unstable elements adding to center forefoot. The results showed that adjusting the unstable elements in coronal plane of forefoot could effectively alter the distribution of plantar pressure, this could potentially offer a mechanism for preventing falling of elderly and rehabilitation of lower extremity malfunctions. This study also demonstrates a novel concept that unstable element could be effectively adjusted in terms of position to meet different functional requirement.

COMPARISON OF GAIT SYMMETRY INDICATORS IN OVERGROUND WALKING AND TREADMILL WALKING USING WIRELESS GYROSCOPES

Gait symmetry has been considered as one of the primary indicators in gait analysis. A symmetrical gait offers several benefits. Among them is a stable and adaptive gait. With wide adoption of wireless inertial sensors i.e., the gyroscope in clinical and rehabilitation settings, this work aimed to investigate the application of symmetry index (SI), symmetry ratio (SR) and normalised symmetry index (SInorm) in defining gait symmetry using measurement collected from wireless gyroscope network. Thigh and shank angular rates during mid-swing, heel-strike and toe-off are used to determine SI, SR and SInorm. In this study, participants were not only instructed to walk naturally on the ground and on a treadmill, but were also requested to walk with restricted knee movement on the ground and on a treadmill to emulate asymmetrical gait. This study demonstrated that the gyroscope can be used to determine gait symmetry. It also shows that SI and SR are not the right indicators to examine gait symmetry using gyroscope data. SI can exceed more than 1,000% at several instances. SR exhibits similar behavior too i.e., it can be as high as 1,000. SInorm performs better in both overground walking and treadmill walking and there are significant difference between symmetrical and asymmetrical gait (p < 0.01).

An ambulatory method of identifying anterior cruciate ligament reconstructed gait patterns

The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.

A stretchable and flexible system for skin-mounted measurement of motion tracking and physiological signals

In this paper, we present a stretchable wearable system capable of i) measuring multiple physiological parameters and ii) transmitting data via radio frequency to a smart phone. The electrical architecture consists of ultra thin sensors (<; 20 μm thick) and a conformal network of associated active and passive electronics in a mesh-like geometry that can mechanically couple with the curvilinear surfaces of the human body. Spring-like metal interconnects between individual chips on board the device allow the system to accommodate strains approaching ~30% A representative example of a smart patch that measures movement and electromyography (EMG) signals highlights the utility of this new class of medical skin-mounted system in monitoring a broad range of neuromuscular and cardiovascular diseases.

Comparing adaptive algorithms to measure temporal gait parameters using lower body mounted inertial sensors

The purpose of this research was to compare different adaptive algorithms in terms of their ability to determine temporal gait parameters based on data acquired from inertial measurement units (IMUs). Eight subjects performed 25 walking trials over a force plate under five different conditions; normal, fast, slow, simulated stiff ankle and simulated stiff knee walking. Data from IMUs worn on the shanks and on the feet were used to identify temporal gait features using three different adaptive algorithms (Green, Selles & Sabatini). Each method's ability to estimate temporal events was compared to the gold standard force plate method for stance time (Greene, r= .990, Selles, r= 0.865, Sabatini, r= 0.980) and double support time (Greene, r= .837, Selles, r= .583, Sabatini, r= .745). The Greene method of estimating gait events from inertial sensor data resulted in the most accurate stance and double support times.

ACCELEROMETRY-BASED LONG-TERM MONITORING OF MOVEMENT DISORDERS: FROM DIURNAL GAIT BEHAVIOR TO NOCTURNAL BED MOBILITY

Accelerometry-based motion analysis is widely recognized as a promising tool in health care and medical settings since it is unobtrusive, inexpensive, and capable of providing useful information on human movement disorders. Patients suffering from neurological diseases such as Parkinson's disease (PD) often exhibit a combination of multiple motion symptoms during everyday activities. Thus, there is a need in clinical practice to capture as many types of abnormal movements as possible with minimal instrumentation that does not interfere with the subject's usual behavioral patterns. This paper presents the prospect of total health monitoring with a single accelerometer-based technique. The behavior of a PD patient was continuously recorded for a period of 36 h using a portable device with a triaxial accelerometer worn on the waist. Data were analyzed by newly developed computer programs to extract relevant movement parameters that might underlie pathological motor performance. We found that the state of the disease could be quantified in terms of distinctive aspects such as gait force, synchronization between both legs, and falls during diurnal walking, and turnover and respiration during nocturnal sleep. Our method may be a useful and practical tool that enables refined clinical assessment of the overall health status of patients with motion disorders.

HUMAN ACTIVITY RECOGNITION BY FUSING MULTIPLE SENSOR NODES IN THE WEARABLE SENSOR SYSTEMS

This paper presents a novel method for recognizing human daily activity by fusion multiple sensor nodes in the wearable sensor systems. The procedure of this method is as follows: firstly, features are extracted from each sensor node and subsequently reduced in dimension by generalized discriminant analysis (GDA), to ensure the real-time performance of activity recognition; then, the reduced features are classified with the multiclass relevance vector machines (RVM); finally, the individual classification results are fused at the decision level, in consideration that the different sensor nodes can provide heterogeneous and complementary information about human activity. Extensive experiments have been carried out on Wearable Action Recognition Database (WARD). Experimental results show that if all the five sensor nodes are fused with the adaptive weighted logarithmic opinion pools (WLOGP) fusion rule, we can even achieve a recognition rate as high as 98.78%, which is far more better than the situations where only single sensor node is available or the activity data is processed by state-of-the-art methods. Moreover, this proposed method is flexible to extension, and can provide a guideline for the construction of the minimum desirable system.

HUMAN GAIT BEHAVIOR INTERPRETATION BY A MOBILE HOME HEALTHCARE ROBOT

The ultimate goal of this study is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients' motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs), and meanwhile, relieve therapists from great burden in canonical rehabilitation. In order to achieve this goal, we have developed the following programs/algorithms for monitoring human activities and recognizing human behaviors: (1) control programs for a mobile robot to track and follow a human subject by three different viewpoints; (2) algorithms for analyzing lower limb joint angles from RGB-D images from a Kinect sensor setup at a mobile robot; and (3) algorithms for recognizing human gait behavior. In (1), side viewpoint, front/back viewpoint and a middle angle viewpoint (between two former viewpoints) tracking were developed. In (2), depth image compensation with colored markers was implemented to deal with the skeleton point extraction error caused by mixing-up and frame flying of depth image during tracking and following human subjects by the mobile robot. In (3), we have proposed a hidden Markov model (HMM) based human behavior recognition using lower limb joint angles and trunk angle. Experimental results showed that joint trajectory could be measured and analyzed with high accuracy compared to a motion tracking system, and human behavior could be recognized from the joint trajectory.

TRANSMISSIBILITY OF WHOLE BODY VIBRATION STIMULI THROUGH HUMAN BODY IN DIFFERENT STANDING POSTURES

This study focuses on the transmissibility of whole body vibration stimuli through human body in different standing postures to explore the mechanism in which vibration stimuli could be better used as a regimen for bone loss. Five volunteers were guided to stay at three standing postures and imposed of frequency-adjustable vibration stimuli on the plantar surfaces side-alternately. Motion capture system was used to acquire the vibration signals at head, pelvis, knee up, knee down and ankle, from which the transmissibility of vibration stimuli can be obtained. The results showed that transmissibility of vibration stimuli was closely correlated with frequency and skeletal sites. Transmissibility of vibration stimuli in head was much smaller than any other skeletal sites. Transmissibility in the ankle was always in the vicinity of unit one in all the three postures for the vibration stimuli applied side-alternately on the plantar surfaces of both feet. There was an obvious peak around 9 to 11 Hz in the transmissibility curves for knee joint and pelvis. In the resonant peak, transmissibility of vibration stimuli in knee joint and pelvis both exceeded unit one and reached 150%. As the frequency increased after 11 Hz, transmissibility of vibration stimuli decayed rapidly as a function of frequency and dropped to 25% at 30 Hz. This study may help to gain insight into the interaction mechanism between mechanical vibration stimuli and the responses of human musculoskeletal system.

Wireless accelerometer iPod application for quantifying gait characteristics

The capability to quantify gait characteristics through a wireless accelerometer iPod application in an effectively autonomous environment may alleviate the progressive strain on highly specific medical resources. The iPod consists of the inherent attributes imperative for robust gait quantification, such as a three dimensional accelerometer, data storage, flexible software, and the capacity for wireless transmission of the gait data through email. Based on the synthesis of the integral components of the iPod, a wireless accelerometer iPod application for quantifying gait characteristics has been tested and evaluated in an essentially autonomous environment. The quantified gait acceleration waveforms were wirelessly transmitted using email for postprocessing. The site for the gait experiment occurred in a remote location relative to the location where the postprocessing was conducted. The wireless accelerometer iPod application for quantifying gait characteristics demonstrated sufficient accuracy and consistency.

SENSING OF PATHOLOGICAL TREMOR USING SURFACE ELECTROMYOGRAPHY AND ACCELEROMETER FOR REAL-TIME ATTENUATION

Tremor is the most common movement disorder and it is affecting more and more people as the world is aging. The cost involved is big considering the financial and social impact. This paper explores an assistive technology solution for upper limb pathological tremor compensation. Using both surface electromyography (SEMG) and accelerometer (ACC), a real-time pathological tremor compensation with functional electrical stimulation (FES) is proposed. One advantage of using SEMG is the electromechanical delay (SEMG data precedes the ACC data by 20–100 ms). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by FES. This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. For estimation of tremor parameters and separation between voluntary motion and tremor, an algorithm based on extended Kalman filter (EKF) is proposed. Experimental result from one essential tremor patient has shown 57% reduction in tremor power as measured by the ACC.

Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application

Parkinson's disease represents a chronic movement disorder, which is generally proportionally to age. The status of Parkinson's disease is traditionally classified through ordinal scale strategies, such as the Unified Parkinson's Disease Rating Scale. However, the application of the ordinal scale strategy inherently requires highly specialized and limited medical resources for interpretation. An alternative strategy involves the implementation of an iPhone application that enables the device to serve as a functional wireless accelerometer system. The Parkinson's disease tremor attributes may be recorded in either an effectively autonomous public or private setting, for which the resultant accelerometer signal of the tremor can be conveyed wireless and through email to a remote location for data post-processing. The initial testing and evaluation of the iPhone wireless accelerometer application for quantifying Parkinson's disease tremor successfully demonstrates the capacity to acquire tremor characteristics in an effectively autonomous environment, while potentially alleviating strain on limited and highly specialized medical resources.

Implementation of an iPhone as a wireless accelerometer for quantifying gait characteristics

The capacity to quantify and evaluate gait beyond the general confines of a clinical environment under effectively autonomous conditions may alleviate rampant strain on limited and highly specialized medical resources. An iPhone consists of a three dimensional accelerometer subsystem with highly robust and scalable software applications. With the synthesis of the integral iPhone features, an iPhone application, which constitutes a wireless accelerometer system for gait quantification and analysis, has been tested and evaluated in an autonomous environment. The acquired gait cycle data was transmitted wireless and through email for subsequent post-processing in a location remote to the location where the experiment was conducted. The iPhone application functioning as a wireless accelerometer for the acquisition of gait characteristics has demonstrated sufficient accuracy and consistency.

Towards remote evaluation of movement disorders via smartphones

Recent advances in mobile phone technology have placed an impressive array of sensing and communication equipment at the hands of an ever-growing number of people. One of the areas which can potentially be transformed by the availability of what is essentially a cheap, ubiquitous networked sensor, is that of remote diagnosis of movement disorders, such as Parkinson's disease. This work describes a smartphone-based method for detecting and quantifying the hand tremor associated with movement disorders using signals from the accelerometer and gyroscope embedded in the patient's phone. Our approach is web-based and user-friendly, requiring minimal user interaction. In clinical experiments with twenty subjects, we found that by combining both accelerometer and gyroscope signals, we were able to correctly identify those with hand tremor, using very simple signal metrics.