External Accelerometer Research Articles

Health monitoring of in-cylinder sensors and fuel injectors using an external accelerometer

This paper focuses on the development of a methodology to monitor the health of an engine by detecting any failures in the fuel injectors or in-cylinder pressure sensors using an accelerometer that is non-intrusively mounted on the engine block. A multi-cylinder engine with each cylinder having its own pressure sensor and injector is considered. First, a model relating the combustion component of the measured acceleration signal to the combustion component of in-cylinder pressure is proposed. Then, gains of the model are tuned to reduce the cycle-to-cycle estimation error by analyzing cycle-to-cycle variations with respect to the combustion pressure peak and engine vibration peak. Using the developed model, cylinder combustion pressures are estimated from engine vibration signals with small cycle-to-cycle estimation errors. Subsequently, a health monitoring system that can detect faults in pressure sensors, fuel injectors, and the accelerometers is proposed based on residues obtained from the difference between estimated combustion pressure and measured pressure signals. The source of the failed component can be identified uniquely by analyzing the pattern of residues. The proposed combustion pressure estimation algorithms are validated by extensive evaluation with experimental data obtained by operating a four-cylinder compression-ignition direct-injection engine with a range of experimental data. Finally, the developed health monitoring system is evaluated with various failure scenarios involving faults in the in-cylinder pressure sensor, fuel injector, and accelerometer.

Repeatability of swimming activity of the Patagonian grouper Acanthistius patachonicus based on accelerometry.

The study of repeatability in behaviour and activity level can be used to evaluate inter-individual differences, which are fundamental to assess the resilience of populations to environmental variation. Previous work on repeatability in wild fish populations has largely been based on acoustic telemetry or mark-and-recapture and has revealed repeatable activity patterns over relatively long periods in a number of species. Although accelerometry is a promising tool for investigating the swimming activity of fish in the wild, little is known about the repeatability of accelerometry-based traits in wild fish. Here, we used external accelerometers to investigate the swimming activity of the Patagonian grouper Acanthistius patachonicus, a rocky-reef fish with high site fidelity, which ensures a high recapture rate of accelerometer tags. Accelerometry was used to investigate the short-term repeatability of a number of activity traits, including swimming, hovering, daily median tailbeat frequency, percentage of high tailbeat frequency and total number of tailbeats at different times of the year. We found that all of the variables are repeatable over the daily scale and four out of five variables are repeatable over weekly periods. Overall, our work suggests that these traits are individual-specific for the short time period investigated. In addition, the percentage of time spent in swimming and hovering was greater in the warm season compared to the cold season, suggesting higher activity levels related to higher temperatures. These results suggest that activity traits related to swimming are repeatable and likely related to the physiological state of each individual. Finally, our work shows that accelerometry can be considered a valuable tool to explore inter-individual differences with potential applications for assessing the resilience of wild populations.

State of Art Enhanced Fatigue Meter Adaptable across Aircrafts

This paper discusses the novel design and development of the state of art fatigue meter for the fighter aircrafts. The unique features of the enhanced fatigue meter (eFM) are its capability to be configured for critical parameters, adaptable across different aircrafts and flexible for using internal or external accelerometer. The combat aircrafts carry out a lot of maneuvers while flying. While doing so, these aircrafts experience large range of variations in the accelerations due to gravity i.e. G-variations. This sudden and large change in acceleration due to gravity, as a function of time forms the fatigue loading on the airframe. This fluctuating loading causes fatigue damage to be accumulated in the aircraft structure. Over a period of time, the accumulated fatigue damage leads to development of crack and breakdown of the structure. In order to monitor and keep track of the g-loads experienced by the aircraft and to predict the remaining service life of the aircraft, a system that measures the g-loads encountered by the aircraft every time it flies is essential. The relevant g-load spectrum of the aircraft can be derived from these recorded data.

GNSS/accelerometer integrated deformation monitoring algorithm based on sensors adaptive noise modeling

The Global Navigation Satellite System (GNSS) and accelerometer integrated system can obtain more stable and reliable deformation displacement with the full use of the complementary technical characteristics of these two types of sensors. The Kalman Filter (KF) method is often applied to integrate GNSS three-dimensional (3D) coordinates and high-rate accelerometer measurements. The key to obtaining accurate solutions is the reasonable setting of measurement noise and process noise of KF. However, in some complex deformation monitoring scenarios such as landslides, both GNSS and accelerometers are susceptible to interference from factors like vegetation cover and external dynamic disturbance. Traditional adaptive filtering methods is hard to simultaneously and appropriately adjust the two types of noise matrices. To address this problem, an adaptive noise model based on sensors is proposed. The adaptive measurement noise is constructed using the posterior coordinate covariance derived from GNSS-RTK process. The random walk coefficient corresponding to the process noise is estimated online via the standard deviation (STD) of acceleration time-varying information. A series of numerical experiments about simulated three-dimensional (3D) deformation displacement are carried out to validate the proposed algorithm. The results show that: (1) When the GNSS signal is disturbed by complex environment, the adaptive measurement noise model effectively contributes to the accuracy improvement, from decimeter level to centimeter level. (2) In the presence of unstable deformable bodies, the adaptive process noise algorithm significantly suppresses the divergent deformation time series caused by external accelerometer disturbances. (3) Even if suffering the overlying effects of electromagnetic interference and external disturbance, the centimeter-level deformation monitoring accuracy can be achieved by using the joint adaptive measurement and process noise model. The improvement effect is particularly significant in the main deformation direction.

Use of Accelerometry as an Educational Tool for Spinal Cord Stimulation: A Pilot Study.

Spinal cord stimulation (SCS) is an important option for patients with chronic neuropathic pain. In the United States, a successful SCS trial determines eligibility for SCS implant. Metrics to determine success are often self-reported and subjective, which may limit achievement of patient goals. This study aimed to assess whether patients undergoing SCS implant after successful trial felt that use of external accelerometry prior to implant was a useful educational tool to objectively appraise function and achievement of treatment goals. This was a single center, prospective, pilot study. Sixteen subjects with persistent spinal pain syndrome type 2 underwent a percutaneous SCS trial. Five subjects did not have a successful trial, one expired after the SCS trial, before implant, and one dropped out prior to completion of post-implant follow-up visits. Nine subjects underwent SCS implant and completed the required follow-up visits. All subjects were provided an Actigraph GT3X external accelerometer, worn 7 days prior to the trial to determine baseline physical activity and during the 7-day trial to assess for change in activity from baseline. Results were shared with subjects to individualize goals for therapy. Goal attainment was assessed at 1, 3, and 6 months after implant. Subjects wore the accelerometer again 24 hours before visits to update progress in meeting treatment goals. The primary outcome was satisfaction with using accelerometry as an educational tool to appraise function and guide treatment goals for SCS therapy. Secondary outcomes included physical activity, as captured via accelerometry, as well as validated patient-reported measures of pain severity, physical functioning, and quality-of-life. Eight of nine subjects were satisfied with accelerometry as an educational tool. Secondary outcomes were not reliably assessed due to poor stewardship and study execution. External accelerometry may assist patients in developing individualized functional treatment goals for SCS therapy.

Comparative Analysis of Physical Activity Detected via an External Accelerometer and Cardiac Implantable Electronic Devices.

BackgroundPhysical activity (PA) has become an important health issue for decades. Cardiovascular implantable electronic devices (CIEDs) have built-in PA-recording functions. We aimed to compare PA measurements using an external accelerometer (ActiGraph GT3X+) and internal accelerometers (Abbott, Biotronik, and Medtronic CIEDs).MethodsThis was a prospective, single-center observational study. The device-measured 7-day average PA was collected, and GT3X+ -measured 7-day average PA was used as the gold-standard, including all daily observations of activity. Pearson’s correlation coefficients were used to compare the correlations between GT3X+ -measured and CIED-measured PA. Bland-Altman plots were used to analyze measurement agreement, and intraclass correlation coefficients were used to analyze reliability.ResultsIn total, 720 patients treated with CIEDs were surveyed between November 2020 and April 2021, 60 of them were analyzed after patient screening by our protocol. Each manufacturer included 20 patients for the final analysis. The CIED-measured PAs of Abbott, Biotronik, and Medtronic were 3.0 ± 1.5, 2.6 ± 1.8, and 3.8 ± 2.5 h per day, respectively; the GT3X+ -measured PAs were 6.9 ± 2.8, 6.0 ± 2.4, and 6.4 ± 2.5 h per day, respectively. Moderate and significant correlations were found in patients using Abbott, Biotronik, and Medtronic CIEDs (r = 0.534, p = 0.015; r = 0.465, p = 0.039; r = 0.677, p = 0.001, respectively). Bland-Altman plots and intraclass correlation coefficients both showed a significant correlation and reliability between the average PA measured by GT3X+ and CIEDs (hours per day).ConclusionAlthough the PA recording function of CIEDs includes a single-axis accelerometer, it has a moderate correlation compared with the triaxial accelerometer of the GT3X+. However, CIEDs seem to underestimate PA for 3–4 h compared to the GT3X+.

Monitoring Particle Movement under Compaction using SmartRock Sensor: A Case Study of Granular Base Layer Compaction

The quality of base course compaction significantly influences pavement’s overall performance. Compared with conventional compaction methods, the intelligent compaction (IC) uses external accelerometers to track pavement responses during compaction and correlates them with pavement layer characteristics to improve the compaction quality. Very few studies have attempted to present interactions of granular materials from the particle level, i.e., how individual particles are responding under the compactive effort. The recent development of a prototype wireless sensor, SmartRock, made tracking the particle movement at meso-scale possible. Accordingly, this paper has the objective of applying the SmartRock sensors to monitor the motion characteristics (i.e., translation and rotation) of granular particles during the compaction process and evaluate the particle response patterns as insights into understanding the compaction mechanism of the unbound aggregate base layer. The experimental site selected was an airport runway test section, designated as the Smart Runway project located in Hill Air Force Base (AFB) in Layton, Utah, United States. SmartRocks were embedded at the bottom of the granular base layer to collect particle motion data during compaction. Analysis results indicated that particle’s motion behavior was closely related to the compactive effort and material properties. Compaction is a process of gaining interlocking among particles and elasticity in materials, and is achieved through the particle’s three-axial angular position arrangement and vertical translation. The particle movement characteristics observed in the granular base were compared to those in asphalt mixtures, which confirmed the unique responses of bound and unbound pavement layer materials. It is suggested that the characteristics of particle rotation behavior during compaction can potentially be used as a control parameter to monitor compaction conditions.

Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone

Induction motors are key pieces of equipment in today’s society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors’ knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.

Feedback Control of Upright Seating with Functional Neuromuscular Stimulation during a Functional Task after Spinal Cord Injury: A Case Study.

Seated stability is a major concern of individuals with trunk paralysis. Trunk paralysis is commonly caused by spinal cord injuries (SCI) at or above the thoracic spine. Current methods to improve stability restrict the movement of the user by constraining their trunk to an upright position. Feedback control of functional neuromuscular stimulation (FNS) can help maintain seated stability while still allowing the user to perform movements to accomplish functional tasks. In this study, an individual with a SCI (C7, AIS B) and an implanted stimulator capable of recruiting trunk and hip musculature unilaterally moved a weighted jar on a countertop to and from three prescribed stations directly in front, laterally, and across midline. For comparison, the tasks were performed with constant baseline stimulation and with feedback modulated stimulation based on the tilt of the trunk obtained from an external accelerometer fed into two PID controllers; one for forward trunk pitch and the other for lateral roll. The trunk pitch and roll angles were obtained through motion capture cameras and various measures of postural sway (95% fitted ellipse area, root mean squared (RMS), path length) and the repeatability (coefficient of variation (CoV), variance ratio (VR)) were calculated. Feedback control significantly increased RMS of trunk movement along the major axis of the fitted ellipse, but decreased RMS values during bending along the minor axis of motion. As a result, the fitted ellipse area decreased when deploying the jar to one of the stations and increased with the other two. The CoV indicated reduced variation in the presence of feedback controlled stimulation for all stations, and VR showed higher repeatability in trunk pitch. Plots of the trunk pitch and roll revealed a faster return to upright motion due to feedback stimulation.Clinical relevance- Feedback control in combination with FNS is a viable method to improve seated stability while still allowing dynamic movements in individuals with a SCI, thus addressing a major concern of the population.

Intelligent Fault Detection, Diagnosis and Health Evaluation for Industrial Robots

The focus of this study is development of an intelligent fault detection, diagnosis and health evaluation system for real industrial robots. The system uses principal component analysis based statistical process control with Nelson rules for online fault detection. Several suitable Nelson rules are chosen for sensitive detection. When a variation is detected, the system performs a diagnostic operation to acquire features of the time domain and the frequency domain from the motor encoder, motor current sensor and external accelerometer for fault diagnosis with a multi-class support vector machine. Additionally, a fuzzy logic based robot health index generator is proposed for evaluating the health of the robot, and the generator is an original design to reflect the health status of the robot. Finally, several real aging-related faults are implemented on a six-axis industrial robot, DRV90L7A6213N by Delta Electronics, and the proposed system is validated effectively by the experimental results.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.

Reducing the impact of intensive care unit mattress compressibility during CPR: a simulation-based study

BackgroundThe depth of chest compression (CC) during cardiac arrest is associated with patient survival and good neurological outcomes. Previous studies showed that mattress compression can alter the amount of CCs given with adequate depth. We aim to quantify the amount of mattress compressibility on two types of ICU mattresses and explore the effect of memory foam mattress use and a backboard on mattress compression depth and effect of feedback source on effective compression depth.MethodsThe study utilizes a cross-sectional self-control study design. Participants working in the pediatric intensive care unit (PICU) performed 1 min of CC on a manikin in each of the following four conditions: (i) typical ICU mattress; (ii) typical ICU mattress with a CPR backboard; (iii) memory foam ICU mattress; and (iv) memory foam ICU mattress with a CPR backboard, using two different sources of real-time feedback: (a) external accelerometer sensor device measuring total compression depth and (b) internal light sensor measuring effective compression depth only. CPR quality was concurrently measured by these two devices. The differences of the two measures (mattress compression depth) were summarized and compared using multilevel linear regression models. Effective compression depths with different sources of feedback were compared with a multilevel linear regression model.ResultsThe mean mattress compression depth varied from 24.6 to 47.7 mm, with percentage of depletion from 31.2 to 47.5%. Both use of memory foam mattress (mean difference, MD 11.7 mm, 95%CI 4.8–18.5 mm) and use of backboard (MD 11.6 mm, 95% CI 9.0–14.3 mm) significantly minimized the mattress compressibility. Use of internal light sensor as source of feedback improved effective CC depth by 7–14 mm, compared with external accelerometer sensor.ConclusionUse of a memory foam mattress and CPR backboard minimizes mattress compressibility, but depletion of compression depth is still substantial. A feedback device measuring sternum-to-spine displacement can significantly improve effective compression depth on a mattress.Trial registrationNot applicable. This is a mannequin-based simulation research.

Effect of weight and frontal area of external telemetry packages on the kinematics, activity levels and swimming performance of small-bodied sharks.

This study sought to observe the effects of submerged weight and frontal cross-sectional area of external telemetry packages on the kinematics, activity levels and swimming performance of small-bodied juvenile sharks, using lemon sharks Negaprion brevirostris (60-80 cm total length, LT ) as a model species. Juveniles were observed free-swimming in a mesocosm untagged and with small and large external accelerometer packages that increased frontal cross-sectional area of the animals and their submerged weight. Despite adhering to widely used standards for tag mass, the presence of an external telemetry package altered swimming kinematics, activity levels and swimming performance of juvenile N. brevirostris relative to untagged individuals, suggesting that tag mass is not a suitable standalone metric of device suitability. Changes in swimming performance could not be detected from tail-beat frequency, which suggests that tail-beat frequency is an unsuitable standalone metric of swimming performance for small N. brevirostris. Lastly, sharks experienced treatment-specific changes in activity level and swimming kinematics from morning to afternoon observation. Therefore, the presence of external telemetry packages altered the kinematics, activity levels and swimming performance of small young-of-the-year N. brevirostris and these data may therefore be relevant to other similar-sized juveniles of other shark species.

A comparative analysis between external accelerometer and internal accelerometer’s physical activity data from implanted resynchronization devices in patients with heart failure

IntroductionThe present study aimed to define changed in percentage physical activity from implanted devices using telemetry, and converting them into MET values (metabolic equivalent), in order to determine oxygen consumption during physical activity. MethodsA total of seventeen patients with heart disease; 3 females and 14 males; age: 57.35yrs±9.54; body mass 98.71±9.89kg; average BMI 36.69±3.67 were recruited into the study. The measured values from Cardiac Resynchronisation Therapy devices and outer accelerometers (ActiGraph GT3X+) were studied over a 7-day time period. Using the two sets of values describing physical performance, linear regression was calculated providing a mathematical equation, thus, the Physical Activity values in percentage were converted into MET values. ResultsDuring the 6-min walk test (6MWT) the patients achieved an average of 416.6±48.2 m. During 6MWT the measured values averaged at 1.85±0.18 MET’s, and MET values averaged at 1.12±0.06 per week. It clearly shows that this test is a challenge for the patients compared to their daily regular physical activity levels. ConclusionsWith our method, based on the values received from the physical activity sensor implanted into the resynchronisation devices, changes in patients’ health status could be monitored telemetrically with the assistance from the implanted electronic device.

Using Smartphone Sensors to Accurately Measure Ground Reaction Forces during Physical Activity - A Feasibility Study

Evidence supports the use of external accelerometers to estimate ground reaction forces (GRFs). Modern smartphones come equipped with a tri-axial accelerometer and thus potentially provide a more cost-effective and convenient means to track GRFs. The ability of smartphone accelerometers to accurately estimate GRFs has not been established. PURPOSE: To compare the ability of smartphone accelerometers to other commercially available accelerometers in estimating GRFs during walking and running. METHODS: Nine subjects had a custom smartphone application downloaded onto their phones. This application allowed access to and recording of the smartphone’s raw accelerometer data. The subject’s smartphone (SP), as well as two other commercial accelerometers (A1 and A2) were placed on the most lateral aspect of the subject’s iliac crest. Subjects walked on an instrumented treadmill (measuring GRF) at a self-selected pace (SSW) for one minute and ran on the instrumented treadmill at a self-selected pace (SSR) for one minute. Using dynamic equations of motion, GRFs were estimated from accelerometer data collected from the three accelerometers (SP, A1, A2). The accuracy of the three accelerometers to predict GRF was calculated as the root mean square difference (RMSD) between resultant GRF from the instrumented treadmill and resultant GRF estimated from the accelerometers across the middle 30 s of the one minute SSW and SSR trials. RMSDs were normalized to average treadmill resultant GRF across the 30 second trials. Two separate one-way ANOVAs were used to assess differences in normalized RMSD between the accelerometers (SP, A1, A2) for both the SSW and SSR trials. RESULTS: There were no significant differences in normalized RMSD between the three accelerometer predictions of GRF during SSW (normalized RMSD - SP: 0.181±0.055, A1: 0.151±0.030, A2: 0.153±0.030, p=0.215) or SSR (normalized RMSD - SP: 0.280±0.087, A1: 0.228±0.036, A2: 0.233±0.043, p=0.126). CONCLUSION: Smartphone accelerometer predicted GRF data did not differ from that provided by previously validated commercial accelerometers. Smartphone accelerometers thus potentially offer a valid means of tracking GRFs through the design of appropriate applications.

The behaviour and recovery of juvenile lemon sharks Negaprion brevirostris in response to external accelerometer tag attachment.

Behavioural responses of lemon sharks Negaprion brevirostris to a fin-mounted tag package (CEFAS G6A tri-axial accelerometer with epoxied Sonotronics PT4 acoustic transmitter) were measured in a controlled captive environment (n = 10, total length, LT range 80-140 cm) and in free-ranging sharks upon release (n = 7, LT range 100-160 cm). No changes were detected in behaviour (i.e. swimming speed, tailbeat frequency, time spent resting and frequency of chafing) between control and tagged captive shark trials, suggesting that the tag package itself does not alter behaviour. In the free-ranging trials, an initial period of elevated swimming activity was found in all individuals (represented by overall dynamic body acceleration). Negaprion brevirostris, however, appeared to recover quickly, returning to a steady swimming state between 2 and 35 min after release. Post-release tracking found that all sharks swim immediately for the shoreline and remain within 100 m of shore for prolonged periods. Hence, although N. brevirostris are capable of quick adaptation to stressors and demonstrate rapid recovery in terms of activity, tracking data suggest that they may modify their spatial use patterns post release. This research is important in separating deviation in behaviour due to environmental stressors from artefacts caused by experimental techniques.

A comparison of public datasets for acceleration-based fall detection

Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range.

Active suppression of structural chatter vibrations using machine drives and accelerometers

Chatter vibrations coming from the machine tool structure are a major limitation for heavy duty milling operations. Passive and active dampers can be added to increase the productivity. However, the price and required space of these absorbers are important drawbacks for their use in the industry. In this work, the machine tool's own drives are used to suppress chatter with the help of an external accelerometer located close to the tool centre point. The measured acceleration is fed back as an additional control loop. A significant increase in the chatter stability limit is demonstrated experimentally on a large milling machine.

Investigation of an acoustic-mechanical method to detect implant loosening

Precise diagnosis of loosening of endoprosthetic implants plays an important role in timely, adequate treatment. However, today's diagnostic tools, mainly radiological evaluation, do not yield satisfactory results. Therefore, a new approach based on the transmission of an internally generated combined acoustic and vibration signal, was investigated. For signal generation, a magnetic oscillator which impinges inside an implant component, can be used. The signal can be detected by an external accelerometer being positioned on the skin surface. Differences in the signal generated result from varying degrees of implant fixation. This principle was tested in seven porcine foreleg specimens with a custom implant. Influence of the measurement location at the porcine skin and different levels of fixation were investigated (pressfit, slight loosening, advanced loosening), with regard to the pull-out strength. Evaluation of different parameters, especially the frequency spectrum resulted in differences of up to 12% for the comparison between pressfit and slight loosening, and 30% between pressfit and advanced loosening. A significant correlation between the measured frequency and the pull-out strength for different levels of fixation was found. Thus, the novel diagnostic method shows potential for the precise detection of implant loosening. Further work is required to determine the sensitivity and specificity of the proposed system in animal experiments.

Accuracy and Characteristics of 2012 Honda Event Data Recorders from Real-Time Replay of Controller Area Network (CAN) Traffic

Prior EDR testing methodologies required setting events in the airbag control module in the vehicle during controlled driving behavior. Duplicating events was nearly impossible, and it was difficult to separate how much differences in recorded speeds to reference speeds was due to measurement error, wheel slip, reporting time delays, or data truncation within the EDR. Recording thresholds have also increased making non-deployment and deployment events closer in magnitude, increasing the risk of accidentally exceeding the deployment threshold while setting events. The new methodology eliminates the risk of accidentally deploying airbags while gathering GPS and CAN bus data in the test vehicle. The techniques presented in this paper also allows gathering of data in vehicle without tampering with the airbag control module, which reduces the potential liability to testers using rental or borrowed test vehicles. The new methodology allows for repeatable testing and mapping the transfer function between the vehicle CAN bus data and the EDR. Should a manufacturer make a design change to an ACM EDR, identical inputs can be given to exemplar ACM's from before and after the changes to document any change in the transfer function. This methodology allows researchers the ability to re-create events of interest in a low-cost, repeatable manner. The accuracy of the 2012 Honda CR-V and 2012 Honda Civic event data recorders were tested using this new two part methodology. First, the test vehicles were instrumented with both a Racelogic VBOX differential GPS speed measurement system and a Vector CAN data logger. The measurements from the VBOX were transmitted onto the vehicle's Controller Area Network (CAN) bus that also contained messages reflecting indicated vehicle speed, brake status, accelerator pedal position, steering wheel angle, individual wheel speeds and other signals. This put the GPS speed data on the same time base as the vehicle CAN speed signal such that no additional synchronization was required. This permitted analysis of the accuracy and update rate of the vehicle speed CAN signal, which is the source for speed data used in the Event Data Recorder (EDR). Second, a system was developed to replay the recorded CAN data to an exemplar airbag control module in the laboratory, such that the exemplar was receiving data exactly as if it were in a moving vehicle. A pneumatic fixture with a slide was built to allow the exemplar module to be accelerated to nearly 10 km/h (6 mph) and then stopped in about 80 msec. to create a non-deployment event that met the minimum 5 mph delta-V over 150ms threshold. Actuation of the event setting fixture was computer controlled (using LabVIEW) and synchronized with the CAN replay system so that the desired test condition could be replicated precisely. An external accelerometer was mounted to the airbag control module to mark when the ACM began recording and the accelerometer data was matched to the acceleration and delta-V data in the EDR. The desired test conditions were replayed to the airbag control module and a series of non-deployment events were set. Each event on the EDR data was read using the Bosch Crash Data Retrieval system. The EDR data was compared to the inputs, and it was determined that the two byte vehicle CAN bus signal was truncated to the next lower whole km/h when recorded in the EDR. Under steady state conditions the speed data was accurate within 2%. The vehicle CAN signal published new values every 0.1 seconds, and the CR-V updated values every 0.1 seconds, but the Civic only updated every 0.6 seconds during hard brake events. During hard ABS braking events the expected wheel slip led to the vehicle speed signal under-reporting ground speed, but the delay in the CAN bus update rate resulted in over-reporting ground speed in many of the tests. Language: en