An adaptive orientation misalignment calibration method for shoulder movements using inertial sensors : a feasibility study

Qualitative assessment of the progress in physical rehabilitation largely depends on accurate measurement of the range of movements and other kinematic parameters. In clinical practice, wearable inertial sensors have proved to be a potential candidate for such measurements, over the traditional marker based optical systems due to cost and space considerations. The accuracy of wearable sensors have a significant dependence on the initial orientation calibration and the assumption that the sensor will not slip or move with respect to the attached limb. This article introduces a novel calibration algorithm to correct initial orientation misalignment, as well as to track and correct subsequent alignment errors progressively throughout the experiment. The theoretical assertions are validated through controlled experiments with simulated accelerometer and gyroscope measurements.

The study to track human arm kinematics applying solutions of Wahba’s Problem upon inertial/magnetic sensors

Long-term, off-site human monitoring systems are emerging with respect to the skyrocketing expenditures engaged with rehabilitation therapies for neurological diseases. Inertial/magnetic sensor modules are well known as a worthy solution for this problem. Much attention and effort are being paid for minimizing drift problem of angular rates, yet the rest of kinematic measurements (earth’s magnetic field and gravitational orientation) are only themselves capable enough to track movements applying the theory for solving historicalWahbas Problem. Further, these solutions give a closed form solution which makes it mostly suitable for real time Mo-Cap systems. This paper examines the feasibility of some typical solutions of Wahba’s Problem named TRIAD method, Davenport’s q method, Singular Value Decomposition method and QUEST algorithm upon current inertial/magnetic sensor measurements for tracking human arm movements. Further, the theoretical assertions are compared through controlled experiments with both simulated and actual accelerometer and magnetometer measurements.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Security considerations for wireless carrier agonistic bio-monitoring systems

Advances in information and communications technology has led to a significant advances in noncontact portable devices capable of monitoring vital signals of patients. These wearable and implantable bio-monitoring systems allow collections of wearable sensors to be constructed as a Body Area Network (BAN) to record biological data for a subject. Such systems can be used to improve the quality of life and treatment outcomes for patients. One of the main uses for a bio-monitoring system is to record biological data values from a subject and provide them to a doctor or other medical professional. However, wearable bio-monitoring systems raise unique security considerations. In this paper, we discuss some of the security considerations that have arisen in our work around communications agnostic bio-monitoring, and how we have addressed these concerns. Furthermore, the issues related to the identifying and trusting sender and receiver entities are discussed.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Fabrication of force sensor circuits on wearable conductive textiles

This paper discusses design and fabrication processes in the development of a wearable and flexible conductive resistive sensor. The design and development of the sensor involve the use of Sn-Ag-Cu (SAC)plated Nylon fabric, precisionfused deposition modeling(FDM) using silicone and petrolatum for etch-resistant masks using the EnvisionTEC GmbH Bioplotter, and wet etching using Chromium, Ammonium Persulphate, and Salt-Vinegar etching solutions. Preliminary testing with other mask types, development processes, and sensor design approaches for various applications are discussed.

Inference of personal sensors in the internet of things

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall

Preliminary research has suggested that wearable cameras may reduce under-reporting of energy intake (EI) in self-reported dietary assessment. The aim of the present study was to test the validity of a wearable camera-assisted 24 h dietary recall against the doubly labelled water (DLW) technique. Total energy expenditure (TEE) was assessed over 15 d using the DLW protocol among forty adults (n 20 males, age 35 (sd 17) years, BMI 27 (sd 4) kg/m2 and n 20 females, age 28 (sd 7) years, BMI 22 (sd 2) kg/m2). EI was assessed using three multiple-pass 24 h dietary recalls (MP24) on days 2-4, 8-10 and 13-15. On the days before each nutrition assessment, participants wore an automated wearable camera (SenseCam (SC)) in free-living conditions. The wearable camera images were viewed by the participants following the completion of the dietary recall, and their changes in self-reported intakes were recorded (MP24+SC). TEE and EI assessed by the MP24 and MP24+SC methods were compared. Among men, the MP24 and MP24+SC measures underestimated TEE by 17 and 9%, respectively (P< 0.001 and P= 0.02). Among women, these measures underestimated TEE by 13 and 7%, respectively (P< 0.001 and P= 0.004). The assistance of the wearable camera (MP24+SC) reduced the magnitude of under-reporting by 8% for men and 6% for women compared with the MP24 alone (P< 0.001 and P< 0.001). The increase in EI was predominantly from the addition of 265 unreported foods (often snacks) as revealed by the participants during the image review. Wearable cameras enhance the accuracy of self-report by providing passive and objective information regarding dietary intake. High-definition image sensors and increased imaging frequency may improve the accuracy further.

Strain sensors with adjustable sensitivity by tailoring the microstructure of graphene aerogel/PDMS nanocomposites

Strain sensors with high elastic limit and high sensitivity are required to meet the rising demand for wearable electronics. Here, we present the fabrication of highly sensitive strain sensors based on nanocomposites consisting of graphene aerogel (GA) and polydimethylsiloxane (PDMS), with the primary focus being to tune the sensitivity of the sensors by tailoring the cellular microstructure through controlling the manufacturing processes. The resultant nanocomposite sensors exhibit a high sensitivity with a gauge factor of up to approximately 61.3. Of significant importance is that the sensitivity of the strain sensors can be readily altered by changing the concentration of the precursor (i.e., an aqueous dispersion of graphene oxide) and the freezing temperature used to process the GA. The results reveal that these two parameters control the cell size and cell-wall thickness of the resultant GA, which may be correlated to the observed variations in the sensitivities of the strain sensors. The higher is the concentration of graphene oxide, then the lower is the sensitivity of the resultant nanocomposite strain sensor. Upon increasing the freezing temperature from −196 to −20 °C, the sensitivity increases and reaches a maximum value of 61.3 at −50 °C and then decreases with a further increase in freezing temperature to −20 °C. Furthermore, the strain sensors offer excellent durability and stability, with their piezoresistivities remaining virtually unchanged even after 10 000 cycles of high-strain loading−unloading. These novel findings pave the way to custom design strain sensors with a desirable piezoresistive behavior.

Fluorescent anion sensors based on 4-amino-1,8-naphthalimide that employ the 4-amino N-H

The new charge neutral 4-amino-1,8-naphthalimide based anion sensors 2 and 3 bind to both acetate and dihydrogenphosphate with 1:1 stoichiometry through hydrogen bonding to both thiourea N–H atoms and in the case of dihydrogenphosphate, the naphthalimide 4 amino N–H group as well. This was clearly established from ¹H NMR titration experiments with H₂PO₄^- in DMSO-d₆ where a substantial shift in the resonance for the naphthalimide N–H was observed concomitant with the expected migration of the thiourea N–H chemical shifts. The binding constants determined from the titration studies indicate that the new sensors bind H₂PO₄^- more strongly than AcO⁻. Fluorescence titrations with sensor 3 indicate quenching of 59% and 36% upon addition of acetate and dihydrogenphosphate, respectively.

Location based power control for energy critical sensors in a disconnected network

This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.

Fluorescent photoinduced electron transfer (PET) sensors for anions; from design to potential application

This mini review highlights the synthesis and photophysical evaluation of anion sensors, for nonaqueous solutions, that have been developed in our laboratories over the last few years. We have focused our research mainly on developing fluorescent photoinduced electron transfer (PET) sensors based on the fluorophore-spacer-anion receptor principle using several anthracene (emitting in the blue) and 1,8-naphthalimide (emitting in the green) fluorophores, with the aim of targeting biologically and industrially relevant anions such as acetates, phosphate and amino acids, as well as halides such as fluoride. The receptors and the fluorophore are separated by a short methyl or ethyl spacer, where the charge neutral anion receptors are either aliphatic or aromatic urea (or thiourea) moieties. For these, the anion recognition is through hydrogen bonding, yielding anion:receptor complexes. Such bonding gives rise to enhanced reduction potential in the receptor moieties which causes enhancement in the rate of PET quenching of the fluorophore excited state from the anion:receptor moiety. This design can be further elaborated on by incorporating either two fluorophores, or urea/thiourea receptors into the sensor structures, using anthracene as a fluorophore. For the latter design, the sensors were designed to achieve sensing of bis-anions, such as di-carboxylates or pyrophosphate, where the anion bridged the anthracene moiety. In the case of the naphthalimide based mono-receptor based PET sensors, it was discovered that in DMSO the sensors were also susceptible to deprotonation by anions such as F− at high concentrations. This led to substantial changes in the absorption spectra of these sensors, where the solution changed colour from yellow/green to deep blue, which was clearly visible to the naked eye. Hence, some of the examples presented can act as dual fluorescent-colorimetric sensors for anions. Further investigations into this phenomenon led to the development of simple colorimetric sensors for fluorides, which upon exposure to air, were shown to fix carbon dioxide as bicarbonate.

Anion recognition and sensing in organic and aqueous media using luminescent and colorimetric sensors

This review article focuses primarily on the work carried in our laboratories over the last few years using luminescent and colorimetric sensors, where the anion recognition occurs through hydrogen bonding in organic or aqueous solvents. This review begins with the story of the discovery of fluorescent photoinduced electron transfer (PET) sensors for anions using charged neutral urea or thiourea receptors where both fluorescent and NMR spectroscopic methods monitored anion recognition. This work led to the development of dual luminescent and colorimetric anion sensors based on the use of the ICT based naphthalimide chromophore, where ions such as fluoride gave rise to changes in both the fluorescence and the absorption spectra of the sensors, but at different concentrations. Here, the former changes were due to hydrogen bonding interactions, whereas the latter was due to the deprotonation of acidic protons, giving rise to the formation of the bifluoride anion (HF2−). Modification of the 4-amino-l,8-naphthalimide moiety has facilitated the formation of colorimetric anion sensors that work both in organic or aqueous solutions. Such charge neutral receptor motifs have also been incorporated into organic scaffolds with norbomyl and calixarene backbones, which have enabled us to produce anion directed self-assembled structures.

Inverse opal gas sensors : Zn(II)-doped tin dioxide systems for low temperature detection of pollutant gases

Focusing here on the effects of zinc doping in a nanocrystalline matrix of tin dioxide, inverse opal prototype sensors are presented and extensively studied as superior candidates for gas sensing applications. Courtesy of factors including controlled porosity, enhanced surface to volume ratio and homogeneous dispersion of species in the crystalline lattice assured by the sol–gel technique, prototype sensors were prepared with high dopant ratios in a range of new compositions. Exploiting their high sensitivities to low-gas concentrations at low working temperatures, and thanks to the presented templated sol–gel approach, the prepared sensors open up new frontiers in compositional control over the sensing oxide materials, consequently widening the possibilities available in on-demand gas sensor synthesis.