Kamen Ivanov

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.

Incremental density-based ensemble clustering over evolving data streams

Abstract The recent advances in smart meter technology have enabled for collecting information about customer power consumption in real time. The measurements are generated continuously and in some cases, e.g. in the industrial smart metering the data exchange rates are highly-fluctuating. The storage, querying, and mining of such smart meter streaming data with a large number of missing and sparse values are highly computationally challenging tasks. To address such matters, we propose a new method called incremental density-based ensemble clustering (IDEStream) for incremental segmentation of various kinds of factories based on their electricity consumption data. It exploits a gamma mixture model to suppress the influence of sparse data units in the data streams that sequentially arrive within a time window and then generates a clustering from the processed data of that window. IDEStream uses a unique incremental ensemble approach to incrementally aggregate the clusterings of subsequent time windows. Experimental results on data streams collected by smart meters from manufacturing factories in Guangdong province of China have shown that the proposed algorithm outperforms several state-of-the-art data stream clustering algorithms. The obtained segmentation can find numerous applications, an exemplar one being to define customer rates in a flexible way.

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.

Estimation and visualization of longitudinal muscle motion using ultrasonography: A feasibility study

Ultrasonography is a convenient and widely used technique to look into the longitudinal muscle motion as it is radiation-free and real-time. The motion of localized parts of the muscle, disclosed by ultrasonography, spatially reflects contraction activities of the corresponding muscles. However, little attention was paid to the estimation of longitudinal muscle motion, especially towards estimation of dense deformation field at different depths under the skin. Yet fewer studies on the visualization of such muscle motion or further clinical applications were reported in the literature. A primal-dual algorithm was used to estimate the motion of gastrocnemius muscle (GM) in longitudinal direction in this study. To provide insights into the rules of longitudinal muscle motion, we proposed a novel framework including motion estimation, visualization and quantitative analysis to interpret synchronous activities of collaborating muscles with spatial details. The proposed methods were evaluated on ultrasound image sequences, captured at a rate of 25 frames per second from eight healthy subjects. In order to estimate and visualize the GM motion in longitudinal direction, each subject was asked to perform isometric plantar flexion twice. Preliminary results show that the proposed visualization methods provide both spatial and temporal details and they are helpful to study muscle contractions. One of the proposed quantitative measures was also tested on a patient with unilateral limb dysfunction caused by cerebral infarction. The measure revealed distinct patterns between the normal and the dysfunctional lower limb. The proposed framework and its associated quantitative measures could potentially be used to complement electromyography (EMG) and torque signals in functional assessment of skeletal muscles.

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.

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

Toward a Smartphone Application for Estimation of Pulse Transit Time

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications.

A statistical MAC protocol for heterogeneous-traffic human body communication

In wireless body sensor networks (WBSN) and wireless body area networks (WBAN), sensor nodes have different bandwidth requirements, therefore, heterogeneous traffic is created. In this paper, we propose a statistical medium access control (MAC) protocol with periodic synchronization for use in heterogeneous traffic networks based on human body communication (HBC). The MAC protocol is designated to ensure energy efficiency by means of flexible time slot allocation and a statistical frame. The statistical frame is intended to increase the sleep time and keep low duty cycles in each beacon period. The MAC protocol was fully implemented on our HBC platform. The experimental results proved that the proposed MAC protocol is compact and energy-efficient.

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.

Exploration and comparison of the pre-impact lead time of active and passive falls based on inertial sensors.

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.