Yanwei Guo

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.

Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.

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 & 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.

A wearable respiratory biofeedback system based on body sensor networks

Technology advantages of body sensor networks (BSN) have shown great deal of promises in medical applications. In this paper we introduced a wearable device for biofeedback application based on the BSN platform we had developed. The biofeedback device we have developed includes the heart rate monitoring belt with conductive fabric and the biofeedback device with respiration belt. A wearable respiratory biofeedback system was preliminarily explored based on the BSN platform. In-situ experiments showed that the BSN platform and the biofeedback device worked as intended.

Use of refined sample entropy and heart rate variability to assess the effects of wearable respiratory biofeedback

Technology advantages of body sensor networks (BSN) have shown great deal of promises in medical applications. In this paper we introduced a wearable device for biofeedback application based on the BSN platform we had developed. The biofeedback device we have developed includes the heart rate monitoring belt with conductive fabric and the biofeedback device with respiration belt. A wearable respiratory biofeedback system was preliminarily explored based on the BSN platform. Due to a large set of temporal scales, HRV cannot be completely characterized on a single time scale, and scaling techniques are required to deeply characterize its behavior. Therefore, refined sample entropy was proposed to assess our respiratory biofeedback effect. In-situ experiments showed that the biofeedback device worked as intended.

Body inertial-sensing network platform for wearable 3D gesture analysis

Gesture analysis was widely used in many applications such as healthcare, robotics and human-computer interactions. This paper presented a low-cost body inertial-sensing network platform developed by us. The platform contains the base station, the BSN inertial measurement nodes and the wireless communication protocol. The sensing nodes contain one 3-axis accelerometer, one 3-axis magnetometer, and one 3-axis gyroscope. Wearable gesture analysis was achieved using this platform. Then Kalman filter was designed to get optimal gesture estimation from the BSN inertial measurement nodes. Preliminary results showed that the averaged estimating errors of the roll angle, the yaw angle and the pitch angle were 3.5°, 3.2°, 2.1°, respectively.