Wireless Inertial Sensor System Research Articles

71 Lameness responses to a single exercise bout following dietary supplementation of Saccharomyces cerevisiae fermentation product in young horses

Yearling Quarter Horses (mean ± SEM; 561 ± 3 d; 373.3 ± 5 kg BW; 13 fillies, 14 colts) were used in a randomized complete block to test the hypothesis that dietary supplementation of a postbiotic Saccharomyces cerevisiae fermentation product (SCFP) mitigates lameness following a single exercise bout in young horses. For the 76-d trial, yearlings were fed a basal diet of 1% BW/d (as-fed) grain-based horse feed formulated without added microbials (SafeChoice® Original, Nutrena) twice daily plus ad libitum Coastal bermudagrass (Cynodon datylon) hay, and water. Horses were grouped by age, sex, and BW and randomly assigned to 1) concentrate only (CON; n = 10), 2) concentrate top dressed with 46 mg/kg SCFP (TRT 1; n = 8), or 3) concentrate top dressed with 92 mg/kg SCFP (TRT 2; n = 9). Objective lameness assessments were obtained by a wireless Inertial Sensor System (ISS) followed by a subjective evaluation performed by a licensed veterinarian. All horses underwent a baseline lameness assessment on d 0, followed by 2 lameness assessments on d 76 (pre-exercise, and post-exercise). Objective lameness evaluations were performed at the trot for 2 30 m, back-and-forth passes, wearing the wireless ISS lameness analysis system and evaluated in triplicate per assessment. For the subjective lameness analysis, horses were additionallytrotted in a circle in both directions within a 16 × 14 m area and evaluated by an individual licensed veterinarian using the American Association of Equine Practitioners (AAEP) lameness scale. Data were analyzed using PROC GLM in SAS v9.4 with diet and time included as fixed effects. When analyzing AAEP lameness evaluations, no differences were detected for dietary treatment or diet × time interactions related to primary (P = 0.55; P = 0.98), secondary (P = 0.43; P = 0.78), or tertiary (P = 0.77; P = 0.77) limbs. Additionally, no dietary treatment effects or diet × time interactions were detected using the wireless ISS for front Q score (P = 0.19; P = 0.92) or hind pushoff Q score (P = 0.53; P = 0.27). Furthermore, there was no effect of dietary treatment on hind impact Q score (P = 0.68). However, there tended to be a diet × time interaction (P = 0.06) whereby horses on TRT 2 tended to have a lower hind impact Q score. Additionally, post-exercise front Q scores increased over time (P ≤ 0.01) compared with both baseline and pre-exercise values regardless of dietary treatment. These results indicate the intensity of exercise may not have been sufficient to lead to dietary differences related to gait asymmetry.

Two-Step Validation of a New Wireless Inertial Sensor System: Application in the Squat Motion

The use of Inertial Measurement Units (IMUs) can provide embedded motion data to improve clinical application. The objective of this study was to validate a newly designed IMU system. The validation is provided through two main methods, a classical sensor validation achieved on a six-degrees-of-freedom hexapod platform with controlled linear and rotation motions and a functional validation on subjects performing squats with segmental angle measurement. The kinematics of the sensors were measured by using an optoelectronic reference system (VICON) and then compared to the orientation and raw data of the IMUs. Bland–Altman plots and Lin’s concordance correlation coefficient were computed to assess the kinematic parameter errors between the IMUs and VICON system. The results showed suitable precision of the IMU system for linear, rotation and squat motions.

Wireless inertial sensor system for hammer throwing

Abstract The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p < 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.

Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System

The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and na&#239ve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.

Objectively measured movement asymmetry in yearling Standardbred trotters

Lameness evaluation of Standardbred trotters can be challenging due to discrepancies in observed movement asymmetry between in-hand and track exercise, and between different trotting speeds. There are few studies on objective measurement of movement in Standardbreds, and little knowledge regarding biological variation and clinical significance of measured movement asymmetry in this breed. To quantify the prevalence and magnitude of objectively measured movement asymmetry in young Standardbred trotters, and identify associations with trainer, sex, height, track type and in-hand measurement prior to or after track trials. Cross-sectional, observational study. A total of 114 Standardbred yearlings were evaluated with a wireless inertial sensor system during trot in-hand and when driven on a track. After exclusions relating to lameness or technical difficulties, 103 horses were included in the study; 77 were evaluated in-hand and on the track, 24 only in-hand and 2 only on the track. Front and/or hindlimb parameters were above asymmetry thresholds previously established for other breeds during in-hand trials for 94 (93%) horses and during track trials for 74 (94%) horses. Most horses showed mild asymmetry. A minority of horses (20%) switched side of the asymmetry for one or more parameters between in-hand and track trials. Mixed model analyses revealed no significant effects of trial mode (in-hand or track trial, in-hand trial pre- or post-track trial, straight or oval track), trainer or horse height. Females had a significant but small reduction in asymmetry in one front limb parameter (HDmax ) compared with males (1.7mm, 95% CI 0.18-3.28, P=.03). High data variability, reflected in large trial standard deviations, relating mainly to a lack of horse compliance. A high proportion of Standardbred yearlings showed movement asymmetries. There was no group-level effect between in-hand and track trials, however, considerable individual variation was observed.

Parametric evaluation of deep brain stimulation parameter configurations for Parkinson's disease using a conformal wearable and wireless inertial sensor system and machine learning.

Deep brain stimulation enables highly specified patient-unique therapeutic intervention ameliorating the symptoms of Parkinson's disease. Inherent to the efficacy of deep brain stimulation is the acquisition of an optimal parameter configuration. Using conventional methods, the optimization process for tuning the deep brain stimulation system parameters can intrinsically induce strain on clinical resources. An advanced means of quantifying Parkinson's hand tremor and distinguishing between parameter settings would be highly beneficial. The conformal wearable and wireless inertial sensor system, such as the BioStamp nPoint, has a volumetric profile on the order of a bandage that readily enables convenient quantification of Parkinson's disease hand tremor. Furthermore, the BioStamp nPoint has been certified by the FDA as a 510(k) medical device for acquisition of medical grade data. Parametric variation of the amplitude parameter for deep brain stimulation can be quantified through the BioStamp nPoint conformal wearable and wireless inertial sensor system mounted to the dorsum of the hand. The acquired inertial sensor signal data can be wirelessly transmitted to a secure Cloud computing environment for post-processing. The quantified inertial sensor data for the parametric study of the effects of varying amplitude can be distinguished through machine learning classification. Software automation through Python can consolidate the inertial sensor data into a suitable feature set format. Using the multilayer perceptron neural network considerable machine learning classification accuracy is attained to distinguish multiple parametric settings of amplitude for deep brain stimulation, such as 4.0 mA, 2.5 mA, 1.0 mA, and 'Off' status representing a baseline. These findings constitute an advance toward the pathway of attaining real-time closed loop automated parameter configuration tuning for treatment of Parkinson's disease using deep brain stimulation.

Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor

Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

A Validation Test of Measurement Method of Lower Limb Angles Based on Kalman Filter on Different Type of Inertial Sensors

Lower limb joint angle measurement method based on Kalman filter was tested using commercially available inertial sensors in our previous study for a rehabilitation use. Although the angle measurement method was effective, measurement with the wireless sensors were affected by wireless communication environment. Wired and wireless inertial sensor systems were developed in our laboratory for solving the wireless communication problem. This paper aimed to examine the 2 developed sensors, since those were different from the previously used sensors in data communication system or resolution in measurement. The 2 developed sensors and 2 commercially available sensors were evaluated in measurements of inclination and joint angles of a rigid body model comparing to the results of an optical motion measurement system. The angles were measured by correcting the angles calculated from outputs of gyroscopes based on a Kalman filter using outputs of accelerometers. All of the sensors showed small RMS errors under the static and dynamic conditions. The results suggested that various inertial sensors could measure inclination and joint angles stably if angles are calculated by using the Kalman filtering based method. It would be possible to replace sensors with inexpensive or latest commercially available sensors.

Heel and Toe Clearance Estimation for Gait Analysis Using Wireless Inertial Sensors

Tripping is considered a major cause of fall in older people. Therefore, foot clearance (i.e., height of the foot above ground during swing phase) could be a key factor to better understand the complex relationship between gait and falls. This paper presents a new method to estimate clearance using a foot-worn and wireless inertial sensor system. The method relies on the computation of foot orientation and trajectory from sensors signal data fusion, combined with the temporal detection of toe-off and heel-strike events. Based on a kinematic model that automatically estimates sensor position relative to the foot, heel and toe trajectories are estimated. 2-D and 3-D models are presented with different solving approaches, and validated against an optical motion capture system on 12 healthy adults performing short walking trials at self-selected, slow, and fast speed. Parameters corresponding to local minimum and maximum of heel and toe clearance were extracted and showed accuracy ± precision of 4.1 ± 2.3 cm for maximal heel clearance and 1.3 ± 0.9 cm for minimal toe clearance compared to the reference. The system is lightweight, wireless, easy to wear and to use, and provide a new and useful tool for routine clinical assessment of gait outside a dedicated laboratory.

Comparison of an inertial sensor system of lameness quantification with subjective lameness evaluation

Subjective evaluation of mild lameness has been shown to have poor interobserver reliability. Traditional methods of objective lameness evaluation require specialised conditions and equipment. Wireless inertial sensor systems have been developed to allow for simple, rapid, objective lameness detection in horses trotted over ground. The purpose of this study was to compare the sensitivities of an inertial sensor system and subjective evaluation performed by experienced equine practitioners at detecting lameness in horses. We hypothesised that the inertial sensor system would identify lameness at a lower level of sole pressure than a consensus of 3 experienced equine veterinarians. Fifteen horses were fitted with special shoes that allowed for lameness induction via sole pressure. Horses were simultaneously evaluated by 3 equine veterinarians and a wireless inertial sensor system. Horses were subjected to multiple trials: 1) before inserting the screw; 2) after inserting the screw to just touch the sole; and 3) after tightening the screw in half turn increments. The number of screw turns required for lameness identification in the correct limb by the inertial sensors and by consensus of 3 equine veterinarians was compared using the Wilcoxon test. The inertial sensor system selected the limb with the induced lameness after fewer screw turns than did the 3 veterinarians (P<0.0001). The inertial sensor system selected the correct limb before the 3 veterinarians in 35 trials (58.33%), the evaluators selected the correct limb before the inertial sensors in 5 trials (8.33%), and in 20 trials (33.33%) they selected the correct limb at the same time. The inertial sensor system was able to identify lameness at a lower level of sole pressure than the consensus of 3 equine veterinarians. The inertial sensor system may be an effective aid to lameness localisation in clinical cases.

A highly miniaturized wireless inertial sensor using a novel 3D flexible circuit

PurposeThe purpose of this paper is to develop a highly miniaturized wireless inertial sensor system based on a novel 3D packaging technique using a flexible printed circuit (FPC). The device is very suitable for wearable applications in which small size and lightweight are required such as body area network, medical, sports and entertainment applications.Design/methodology/approachModern wireless inertial measurement units are typically implemented on a rigid 2D printed circuit board (PCB). The design concept presented here is based around the use of a novel planar, six‐faceted, crucifix or cross‐shaped FPC instead of a rigid PCB. A number of specific functional blocks (such as microelectromechanical systems gyroscope and accelerometer sensors, microcontroller (MCU), radio transceiver, antenna, etc.) are first assigned to each of the six faces which are each 1 cm2 in area. The FPC cross is then developed into a 1 cm3, 3D configuration by folding the cross at each of five bend planes. The result is a low‐volume and lightweight, 1 cm3 wireless inertial sensor that can sense and send motion sensed data wirelessly to a base station. The wireless sensor device has been designed for low power operation both at the hardware and software levels. At the base station side, a radio receiver is connected to another MCU unit, which sends received data to a personal computer (PC) and graphical user interface. The industrial, scientific and medical band (2.45 GHz) is used to achieve half duplex communication between the two sides.FindingsA complete wireless sensor system has been realized in a 3D cube form factor using an FPC. The packaging technique employed during the work is shown to be efficient in fabricating the final cubic system and resulted in a significant saving in the final size and weight of the system. A number of design issues are identified regarding the use of FPC for implementing the 3D structure and the chosen solutions are shown to be successful in dealing with these issues.Research limitations/implicationsCurrently, a limitation of the system is the need for an external battery to power the sensor system. A second phase of development would be required to investigate the possibility of the integration of a battery and charging system within the cube structure. In addition, the use of flexible substrate imposes a number of restrictions in terms of the ease of manufacturability of the final system due to the requirement of the required folding step.Practical implicationsThe small size and weight of the developed system is found to be extremely useful in different deployments. It would be useful to further explore the system performance in different application scenarios such as wearable motion tracking applications. In terms of manufacturability, component placement needs to be carefully considered, ensuring that there is sufficient distance between the components, bend planes and board edges and this leads to a slightly reduced usable area on the printed circuit.Originality/valueThis paper provides a novel and useful method for realizing a wireless inertial sensor system in a 3D package. The value of the chosen approach is that a significant reduction in the required system volume is achieved. In particular, a 78.5 per cent saving in volume is obtained in decreasing the module size from a 25 to a 15 mm3 size.