Guoru Zhao

Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor Network

The unintentional injuries due to falls in elderly people give rise to a multitude of health and economic problems due to the growing aging population. The use of early pre-impact fall alarm and self-protective control could greatly reduce fall injuries. This paper aimed to explore and implement a pre-impact fall recognition/alarm method for free-direction fall activities based on understanding of the pre-impact lead time of falls and the angle of body postural stability using an inertial body sensor network. Eight healthy Asian adult subjects were arranged to perform three kinds of daily living activities and three kinds of fall activities. Nine MTx sensor modules were used to measure the body segmental kinematic characteristics of each subject for pre-impact fall recognition/alarm. Our analysis of the kinematic features of human body segments showed that the chest was the optimal sensor placement for an early pre-impact recognition/alarm (i.e., prediction/alarm of a fall event before it happens) and post-fall detection (i.e., detection of a fall event after it already happened). Furthermore, by comparative analysis of threshold levels for acceleration and angular rate, two acceleration thresholds were determined for early pre-impact alarm (7 m/s/s) and post-fall detection (20 m/s/s) under experimental conditions. The critical angles of postural stability of torso segment in three kinds of fall activities (forward, sideway and backward fall) were determined as 23.9 ± 3.3, 49.9 ± 4.1 and 9.9 ± 2.5 degrees, respectively, and the relative average pre-impact lead times were 329 ± 21, 265 ± 35 and 257 ± 36 ms. The results implied that among the three fall activities the sideway fall was associated with the largest postural stability angle and the forward fall was associated with the longest time to adjust body angle to avoid the fall; the backward fall was the most difficult to avoid among the three kinds of fall events due to the toughest combination of shortest lead time and smallest angle of postural stability which made it difficult for the self-protective control mechanism to adjust the body in time to avoid falling down.

A Low-Cost Body Inertial-Sensing Network for Practical Gait Discrimination of Hemiplegia Patients

Gait analysis is widely used in detecting human walking disorders. Current gait analysis methods like video- or optical-based systems are expensive and cause invasion of human privacy. This article presents a self-developed low-cost body inertial-sensing network, which contains a base station, three wearable inertial measurement nodes, and the affiliated wireless communication protocol, for practical gait discrimination between hemiplegia patients and asymptomatic subjects. Every sensing node contains one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope. Seven hemiplegia patients (all were abnormal on the right side) and 7 asymptomatic subjects were examined. The three measurement nodes were attached on the thigh, the shank, and the dorsum of the foot, respectively (all on the right side of the body). A new method, which does not need to obtain accurate positions of the sensors, was used to calculate angles of knee flexion/extension and foot in the gait cycle. The angle amplitudes of initial contact, toe off, and knee flexion/extension were extracted. The results showed that there were significant differences between the two groups in the three angle amplitudes examined (-0.52±0.98° versus 6.94±2.63°, 28.33±11.66° versus 47.34±7.90°, and 26.85±8.6° versus 50.91±6.60°, respectively). It was concluded that the body inertial-sensing network platform provided a practical approach for wearable biomotion acquisition and was effective for discriminating gait symptoms between hemiplegia and asymptomatic subjects.

Segmental Kinematic Coupling of the Human Spinal Column during Locomotion

As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well understood. However, so far our understanding of the motions and functional contributions of the human spine during locomotion is still very poor and simultaneous in-vivo limb and spinal column motion data are scarce. The objective of this study is to investigate the delicate in-vivo kinematic coupling between different functional regions of the human spinal column during locomotion as a stepping stone to explore the locomotor function of the human spine complex. A novel infrared reflective marker cluster system was constructed using stereophotogrammetry techniques to record the 3D in-vivo geometric shape of the spinal column and the segmental position and orientation of each functional spinal region simultaneously. Gait measurements of normal walking were conducted. The preliminary results show that the spinal column shape changes periodically in the frontal plane during locomotion. The segmental motions of different spinal functional regions appear to be strongly coupled, indicating some synergistic strategy may be employed by the human spinal column to facilitate locomotion. In contrast to traditional medical imaging-based methods, the proposed technique can be used to investigate the dynamic characteristics of the spinal column, hence providing more insight into the functional biomechanics of the human spine.

Sample entropy characteristics of movement for four foot types based on plantar centre of pressure during stance phase.

BackgroundMotion characteristics of CoP (Centre of Pressure, the point of application of the resultant ground reaction force acting on the plate) are useful for foot type characteristics detection. To date, only few studies have investigated the nonlinear characteristics of CoP velocity and acceleration during the stance phase. The aim of this study is to investigate whether CoP regularity is different among four foot types (normal foot, pes valgus, hallux valgus and pes cavus); this might be useful for classification and diagnosis of foot injuries and diseases. To meet this goal, sample entropy, a measure of time-series regularity, was used to quantify the CoP regularity of four foot types.MethodsOne hundred and sixty five subjects that had the same foot type bilaterally (48 subjects with healthy feet, 22 with pes valgus, 47 with hallux valgus, and 48 with pes cavus) were recruited for this study. A Footscan® system was used to collect CoP data when each subject walked at normal and steady speed. The velocity and acceleration in medial-lateral (ML) and anterior-posterior (AP) directions, and resultant velocity and acceleration were derived from CoP. The sample entropy is the negative natural logarithm of the conditional probability that a subseries of length m that matches pointwise within a tolerance r also matches at the next point. This was used to quantify variables of CoP velocity and acceleration of four foot types. The parameters r (the tolerance) and m (the matching length) for sample entropy calculation have been determined by an optimal method.ResultsIt has been found that in order to analyze all CoP parameters of velocity and acceleration during the stance phase of walking gait, for each variable there is a different optimal r value. On the contrary, the value m=4 is optimal for all variables.Sample entropies of both velocity and acceleration in AP direction were highly correlated with their corresponding resultant variables for r>0.91. The sample entropy of the velocity in AP direction was moderately correlated with the one of the acceleration in the same direction (r≥0.673), as well as with the resultant acceleration (r≥0.660). The sample entropy of resultant velocity was moderately correlated with the one of the acceleration in AP direction, as well as with the resultant acceleration (for the both r≥0.689). Moderate correlations were found between variables for the left foot and their corresponding variables for the right foot.Sample entropies of AP velocity, resultant velocity, AP acceleration, and resultant acceleration of the right foot as well as AP velocity and resultant velocity of the left foot were, respectively, significantly different among the four foot types.ConclusionsIt can be concluded that the sample entropy of AP velocity (or the resultant velocity) of the left foot, ML velocity, resultant velocity, ML acceleration and resultant acceleration could serve for evaluation of foot types or selection of appropriate footwear.

Balance and knee extensibility evaluation of hemiplegic gait using an inertial body sensor network

BackgroundMost hemiplegic patients have difficulties in their balance and posture control while walking because of the asymmetrical posture and the abnormal body balance. The assessment of rehabilitation of hemiplegic gait is usually made by doctors using clinical scale, but it is difficult and could not be used frequently. It is therefore needed to quantitatively analyze the characteristics of hemiplegic gait. Thus the assessment would be simple, and real-time evaluation of rehabilitation could be carried out.MethodsTwenty subjects (ten hemiplegic patients, ten normal subjects) were recruited. The subjects walked straight for five meters at their self-selected comfortable speed towards a target line on the floor.Xsens MTx motion trackers were used for acquiring gestures of body segments to estimate knee joint angles and identify gait cycles. A practical method for data acquisition that does not need to obtain accurate distances between a knee joint and its corresponding sensors is presented.ResultsThe results showed that there were significant differences between the two groups in the three nominated angle amplitudes. The mean values of balance level of each parameter in hemiplegic gait and normal gait were: 0.21 versus 0.01, 0.18 versus 0.03, and 0.92 versus 0.03, respectively. The mean values of added angles of each parameter in hemiplegic gait and normal gait were: 74.64 versus 91.31, -76.48 versus −132.4, and 6.77 versus 35.74.ConclusionsIt was concluded that the wearable bio-motion acquisition platform provided a practical approach that was effective in discriminating gait symptoms between hemiplegic and asymptomatic subjects. The extensibility of hemiplegic patients’ lower limbs was significantly lower than that of normal subjects, and the hemiplegic gait had worse balance level compared with normal gait. The effect of rehabilitation training of hemiplegic gait could be quantitatively analyzed.

Analysis of filtering methods for 3D acceleration signals in body sensor network

Development of denoising algorithm for 3D acceleration signals is essential to facilitate accurate assessment of human movement in body sensor networks (BSN). In this study, firstly 3D acceleration signals were captured by self-developed nine-axis wireless BSN platform during 12 subjects performing regular walking. Then, acceleration noise was filtered using four common filters respectively: median filter, Butterworth low-pass filter, discrete wavelet package shrinkage and Kalman filter. Finally, signal-to-noise ratio (SNR) and correlation coefficient(R) between filtered signal and reference signal were determined. We found that (1) Kalman filter showed the largest SNR and R values, followed by median filter, discrete wavelet package shrinkage and finally Butterworth low-pass filter; whereas, after correcting waveform delay for Butterworth low-pass filter, its performance was a little better than that of Kalman filter; (2) Real-time performance of median filter related to its window length; Decomposition level influenced real-time performance of discrete wavelet package shrinkage; Butterworth low-pass filter could bring large waveform delay if filter order and cut-off frequency were not properly selected. The algorithms of these filters would be further investigated to achieve best noise reduction of 3D acceleration signals in future.

Pre-impact and Impact Detection of Falls Using Built-In Tri-accelerometer of Smartphone

Falls in elderlies are a major health and economic problem. Research on falls in elderly people has the great social significance under the population aging. Previous smartphone-based fall detection systems have not both fall detection and fall prevention, and the feasibility has not been fully examined. In this paper, we propose a smartphone-based fall detection system using a threshold-based algorithm to distinguish between Activities of Daily Living (ADL) and falls in real time. The smartphone with built-in tri-accelerometer is used for detecting early-warning of fall based on pre-impact phase and post-fall based on impact phase. Eight healthy Asian adult subjects who wear phone at waist were arranged to perform three kinds of daily living activities and three kinds of fall activities. By comparative analysis of threshold levels for acceleration, in order to get the best sensitivity and specificity, acceleration thresholds were determined for early pre-impact alarm (4.5-5m/s2) and post-fall detection (21-28 m/s2) under experimental conditions.

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients

BackgroundImprovement in muscle strength is an important aim for the rehabilitation of hemiplegia patients. Presently, the rehabilitation prescription depends on the evaluation results of muscle strength, which are routinely estimated by experienced physicians and therefore not finely quantitative. Widely-used quantification methods for disability, such as Barthel Index (BI) and motor component of Functional Independent Measure (M-FIM), yet have limitations in their application, since both of them differentiated disability better in lower than higher disability, and they are subjective and recorded in wide scales. In this paper, to explore finely quantitative measures for evaluation of muscle strength level (MSL), we start with the study on quantified electromyography (EMG) and sonomyography (SMG) features of tibialis anterior (TA) muscles among hemiplegia patients.Methods12 hemiplegia subjects volunteered to perform several sets of plantar-flexion movements in the study, and their EMG signals and SMG signals were recorded on TA independently to avoid interference. EMG data were filtered and then the root-mean-square (RMS) was computed. SMG signals, specifically speaking, the muscle thickness of TA, were manually measured by two experienced operators using ultrasonography. Reproducibility of the SMG assessment on TA between operators was evaluated by non-parametric test (independent sample T test). Possible relationship between muscle thickness changes (TC) of TA and muscle strength level of hemiplegia patients was estimated.ResultsMean of EMG RMS between subjects is found linearly correlated with MSL (R2 = 0.903). And mean of TA muscle TC amplitudes is also linearly correlated with MSL among dysfunctional legs (R2 = 0.949). Moreover, rectified TC amplitudes (dysfunctional leg/ healthy leg, DLHL) and rectified EMG signals (DLHL) are found in linear correlation with MSL, with R2 = 0.756 and R2 = 0.676 respectively. Meanwhile, the preliminary results demonstrate that patients’ peak values of TC are generally proportional to their personal EMG peak values in 12 dysfunctional legs and 12 healthy legs (R2 = 0.521).ConclusionsIt’s concluded that SMG could be a promising option to quantitatively estimate MSL for hemiplegia patients during rehabilitation besides EMG. However, after this exploratory study, they should be further investigated on a larger number of subjects.

Pre-impact & impact detection of falls using wireless Body Sensor Network

Falls in elderlies are a major health and economic problem. This paper aimed at finding the best body position to place inertial sensors and the best feature for pre-impact and impact detection of fall using wireless Body Sensor Network. Waist acceleration maybe the optimal formula for fall detection, under the conditions of existing inertial sensors best precision. We set two thresholds for acceleration, 5 m/s2 could get 500ms lead-time and 35 m/s2 ensure the specificity up to 100%. We also analyzed the critical phase in fall events, and subdivided it into three periods, make a fall event more intuitive to people.