Navid Amini

Accelerometer-based on-body sensor localization for health and medical monitoring applications

In this paper, we present a technique to recognize the position of sensors on the human body. Automatic on-body device localization ensures correctness and accuracy of measurements in health and medical monitoring systems. In addition, it provides opportunities to improve the performance and usability of ubiquitous devices. Our technique uses accelerometers to capture motion data to estimate the location of the device on the users body, using mixed supervised and unsupervised time series analysis methods. We have evaluated our technique with extensive experiments on 25 subjects. On average, our technique achieves 89% accuracy in estimating the location of devices on the body. In order to study the feasibility of classification of left limbs from right limbs (e.g., left arm vs. right arm), we performed analysis, based of which no meaningful classification was observed. Personalized ultraviolet monitoring and wireless transmission power control comprise two immediate applications of our on-body device localization approach. Such applications, along with their corresponding feasibility studies, are discussed.

Smart insole: a wearable system for gait analysis

Gait analysis is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. However, standard human gait analysis has to be performed in a specific gait lab and operated by a medical professional. This traditional method increases the examination cost and decreases the accuracy of the natural gait model. In this paper, we present a novel portable system, called Smart Insole, to address the current issues. Smart Insole integrates low cost sensors and computes important gait features. In this way, patients or users can wear Smart Insole for gait analysis in daily life instead of participating in gait lab experiments for hours. With our proposed portable sensing system and effective feature extraction algorithm, the Smart Insole system enables precise gait analysis. Furthermore, taking advantage of the affordability and mobility of Smart Insole, pervasive gait analysis can be extended to many potential applications such as fall prevention, life behavior analysis and networked wireless health systems.

Cluster size optimization in sensor networks with decentralized cluster-based protocols

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.

eCushion: A Textile Pressure Sensor Array Design and Calibration for Sitting Posture Analysis

Sitting posture analysis is widely applied in many daily applications in biomedical, education, and health care domains. It is interesting to monitor sitting postures in an economic and comfortable manner. Accordingly, we present a textile-based sensing system, called Smart Cushion, which analyzes the sitting posture of human being accurately and non-invasively. First, we introduce the electrical textile sensor and its electrical characteristics, such as offset, scaling, crosstalk, and rotation. Second, we present the design and implementation of the Smart Cushion system. Several effective techniques have been proposed to improve the recognition rate of sitting postures, including sensor calibration, data representation, and dynamic time warping-based classification. Last, our experimental results show that the recognition rate of our Smart Cushion system is in excess of 85.9%.

On-body device localization for health and medical monitoring applications

We present a technique to discover the position of sensors on the human body. Automatic on-body device localization ensures correctness and accuracy of measurements in health and medical monitoring systems. In addition, it provides opportunities to improve the performance and usability of ubiquitous devices. Our technique uses accelerometers to capture motion data to estimate the location of the device on the users body, using mixed supervised and unsupervised time series analysis methods. We have evaluated our technique with extensive experiments on 25 subjects. On average, our technique achieves 89% accuracy in estimating the location of devices on the body.

Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection

Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than 30 percent energy savings while achieving 96.7 percent classification accuracy.

eCushion: An eTextile Device for Sitting Posture Monitoring

Sitting posture analysis is critical for daily applications in biomedical, education and healthcare fields. However, it remains unclear how to monitor sitting posture economically and comfortably. To this end, we presented an eTextile device, called eCushion, in this paper, which can analyze the sitting posture of human being accurately and non-invasively. First, we discussed the implementation of eCushion and design challenges of sensing data, such as scale, offset, rotation and crosstalk. Then, several effective techniques have been proposed to improve the recognition rate of sitting posture. Our experimental results show that the recognition rate of our eCushion system could achieve 92% for object-oriented cases and 79% for general cases.

Influence of correction of ocular magnification on spectral-domain OCT retinal nerve fiber layer measurement variability and performance.

PURPOSE To analyze the influence of ocular magnification on the peripapillary retinal nerve fiber layer (RNFL) thickness measurement and its performance as acquired with spectral-domain optical coherence tomography (SD-OCT). METHODS Spectral domain OCT measurements from 108 normal eyes (59 subjects) and 72 glaucoma eyes (58 patients) were exported and custom software was used to correct RNFL measurements for ocular magnification. Retinal nerve fiber layer prediction limits in normal subjects, structure-function relationships, and RNFL performance for detection of glaucoma were compared before and after correction for ocular magnification (Bennetts formula). Association of disc area with cross-sectional RNFL area was explored. RESULTS The median (interquartile range, [IQR]) visual field mean deviation and scaling factor were 0 (-0.85 to 0.73) dB and 0.96 (0.93-1.00) in normal eyes and -4.0 (-6.0 to -2.2) dB and 0.99 (0.95-1.03) in the glaucoma group (P < 0.001 and P = 0.003, respectively; average correction 3%). Correction for ocular magnification caused a reversal of the negative relationship between the cross-sectional RNFL area and axial length (slope = -0.022 mm(2)/mm, P = 0.015 vs. = 0.22 mm(2)/mm, P = 0.007). However, such correction did not change RNFL prediction limits (except in superior and nasal quadrants), improve global or regional structure-function relationships, or enhance the ability of RNFL measurements to discriminate glaucoma from normal eyes (P > 0.05). The cross-sectional RNFL area was not correlated with optic disc area (P = 0.325). CONCLUSIONS Correction of RNFL measurements for ocular magnification did not improve prediction limits in normal subjects or enhance the performance of SD-OCT in this group of eyes in which the axial length did not deviate significantly from average values. The cross-sectional area of the RNFL was not related to the optic disc area.

Reliability-Aware Optimization for DVS-Enabled Real-Time Embedded Systems

Power and energy consumption has emerged as the premier and most constraining aspect in modern computational systems. Dynamic voltage scheduling (DVS) has been provably one of the most effective techniques used to achieve low power specification. On the other hand, as the feature size of logic gates (and transistors) is becoming smaller and smaller, the effect of soft error rates caused by single event upsets (SEUs) becomes exponentially greater. Lowering supply voltage to save energy increases soft error rates caused by SEU for two reasons: I) lower voltage makes digital circuits more prone to soft errors and II) reduction in supply voltage, increases the duration of process which increases the chances of being hit by SEU. In this paper, we propose an optimal methodology for DVS on a task graph with consideration of soft error rate. We consider the effects of voltage on SEU and incorporate this dependency in our formulation to develop a new method for energy optimization under SEU constraints. We also propose a convex programming formulation that can be solved efficiently and optimally. We show the effectiveness of our optimal results by simulation on TGFF benchmarks.

HERMES: Mobile system for instability analysis and balance assessment

We introduce Hermes, a lightweight smart shoe and its supporting infrastructure aimed at extending gait and instability analysis and human instability/balance monitoring outside of a laboratory environment. We aimed to create a scientific tool capable of high-level measures, by combining embedded sensing, signal processing and modeling techniques. Hermes monitors walking behavior and uses an instability assessment model to generate quantitative value with episodes of activity identified by physician, researchers or investigators as important. The underlying instability assessment model incorporates variability and correlation of features extracted during ambulation that have been identified by geriatric motion study experts as precursor to instability, balance abnormality and possible fall risk. Hermes provides a mobile, affordable and long-term instability analysis and detection system that is customizable to individual users, and is context-aware, with the capability of being guided by experts. Our experiments demonstrate the feasibility of our model and the complimentary role our system can play by providing long-term monitoring of patients outside a hospital or clinical setting at a reduced cost, with greater user convenience, compliance and inference capabilities that meet the physicians or investigators needs.