Zhanyong Mei

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.

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.

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.

An explorative investigation of functional differences in plantar center of pressure of four foot types using sample entropy method.

In the study of biomechanics of different foot types, temporal or spatial parameters derived from plantar pressure are often used. However, there is no comparative study of complexity and regularity of the center of pressure (CoP) during the stance phase among pes valgus, pes cavus, hallux valgus and normal foot. We aim to analyze whether CoP sample entropy characteristics differ among these four foot types. In our experiment participated 40 subjects with normal feet, 40 with pes cavus, 19 with pes valgus and 36 with hallux valgus. A Footscan® system was used to collect CoP data. We used sample entropy to quantify several parameters of the investigated four foot types. These are the displacement in medial–lateral (M/L) and anterior–posterior (A/P) directions, as well as the vertical ground reaction force of CoP during the stance phase. To fully examine the potential of the sample entropy method for quantification of CoP components, we provide results for two cases: calculating the sample entropy of normalized CoP components, as well as calculating it using the raw data of CoP components. We also explored what are the optimal values of parameters m (the matching length) and r (the tolerance range) when calculating the sample entropy of CoP data obtained during the stance phases. According to statistical results, some factors significantly influenced the sample entropy of CoP components. The sample entropies of non-normalized A/P values for the left foot, as well as for the right foot, were different between the normal foot and pes valgus, and between the normal foot and hallux valgus. The sample entropy of normalized M/L displacement of the right foot was different between the normal foot and pes cavus. The measured variable for A/P and M/L displacements could serve for the study of foot function.

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.

Automated detection of plantar pressure ROIs based on multiple frame data

Semi-automatic or manual method were usually used to define the region of interest (ROI) of dynamic plantar pressure for foots biomechanical research. However, the existing methods are time-consuming, complicated, or unrepeatable. So, automatic method to extract ROIs is needed. Moreover, spatial adjacency of ROIs in the forefoot put forward the challenge for automatic methods. The objective of this study is to find out an automatic method for defining the ROIs using dynamic plantar pressure information of multiple frames. Twenty subjects were asked to participate in tests. The plantar pressure data and 3D kinematical data were collected. The maximal inter-frame difference during stand phase was calculated. Then the locations of ROIs were determined by using maximal inter-frame difference and priori knowledge about spatial relation of anatomical location of ROIs. Recognition accuracy by using our method was compared with by using Motion Analysis. The results indicated that using inter-frame difference was able to produce recognition accuracy 100%, 100%, 90%, 100%, 90%, 90% for halluces and metatarsus I to V, respectively, which had highly agreement with the result of 3D motion capture system. Furthermore, our method is simple and discreet, which could be employed in wider applications.

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.

A Custom Base Station for Collecting and Processing Data of Research-Grade Motion Sensor Units

In studies of human biomechanics utilizing inertial sensors, motion sensor units of type Xsens are recognized as the state-of-the-art. However, the requirement to use them with a personal computer for collecting and processing data could be a limiting factor. In the present work, we demonstrate a simple solution to using up to four Xsens MTx units with a custom portable base station. The base station is capable of obtaining data from Xsens MTx units, processing the data and saving them to an SD card. Thus, it allows the use of the units outside laboratory settings without the need of a personal computer, the capability to directly use onboard custom algorithms to process the data of several units in real time, and interconnectivity with external systems for synchronized collection of multimodal data. We demonstrate these benefits by two examples: synchronized collection of data from an Xsens MTx unit and a Footscan® plantar pressure plate, and knee angle measurement using two Xsens MTx units that we validated by synchronized recording of goniometric data.

Kinematic design of a parallel ankle rehabilitation robot for sprained ankle physiotherapy

Parallel robot was widely applied in the field of medical rehabilitation. Particularly, the ankle parallel rehabilitation robot was known as the hot research topics. The paper introduced the parallel robot in the ankle rehabilitation applications, then ankle physiological structure, damage mechanism were analyzed. It presented a novel parallel ankle rehabilitation robot, and achieved the kinematic solution and simulation analysis. The results showed that: (1) In the single input condition, the range of motion for the moving platform to pull the ankle of patients were dorsiflexion (0°-30°), plantar flexion (0°-50°), inversion/ eversion(0°-18°), adduction/ abduction (0°-10°), which was suitable for the patients in initial rehabilitation training of ankle. (2) In double inputs condition, the range of motion for the moving platform were pulled the ankle of patients were dorsiflexion (0°-30°), plantar flexion (0°-50°), inversion/ eversion (0°-25°), adduction/ abduction (0°-20°), which was suitable for the patients in medium-term rehabilitation training of ankle. (3) In three inputs condition, the range of motion for the moving platform were pulled the ankle of patients were dorsiflexion (0°-30°), plantar flexion (0°-50°), inversion/ eversion (0°-40°), adduction/ abduction (0°-30°), which was more conducive to the rehabilitation training for the ankle of patients. According to these characteristics, it was properly to meet the range of motion for the normal ankle, and helped patients to take various kinds of ankle rehabilitation exercising.