Use of a Collar-Mounted Triaxial Accelerometer to Predict Speed and Gait in Dogs.
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ABSTRACT:
SUBMITTER: Bolton S
PROVIDER: S-EPMC8146851 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Early detection of disease by an animal owner may motivate them to seek early veterinary advice. Presentation before a more advanced clinical manifestation is evident could lead to more effective treatment and thus benefit the animal's health and welfare. Accelerometers are able to detect changes in specific activities or behaviours, thus indicating early signs of possible adverse health events. The objective of this validation study was to determine whether the detection of eight behavioural states: walk, trot, canter/gallop, sleep, static/inactive, eat, drink, and headshake, by an accelerometer device was sufficiently accurate to be useful in a clinical setting. This fully independent external validation estimated the accuracy of a specific triaxial, collar-mounted accelerometer on a second-by second basis in 51 healthy dogs of different breeds, aged between 6 months and 13 years, weighing >10 kg. The overall diagnostic effectiveness was estimated as: % record correctly classified of > 95% in walk, trot, canter/gallop, eat, drink and headshake and >90% in sleep and static/inactive. The positive predictive values ranged from 93-100%, while the negative predictive values ranged from 96-100%, with exception of static/inactive (86%).This was probably because dogs were placed in unfamiliar kennels where they did not exhibit their typical resting behaviour. The device is worn on a collar, making its use feasible for anyone wanting to monitor their dog's behaviour. The high accuracy in detecting various kinds of behaviour appears promising in assessing canine health and welfare states.
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Project description:BACKGROUND:The majority of dogs with idiopathic epilepsy continue to have seizures despite appropriate treatment. OBJECTIVES:To assess the use of a commercially available, collar-mounted accelerometer to detect generalized seizures in dogs. ANIMALS:Twenty two client-owned dogs with idiopathic epilepsy. METHODS:Six-month prospective clinical study during which dogs wore a collar-mounted accelerometer. Seizure documentation was based on owner observations and video recordings. The accelerometer used a predefined algorithm to detect seizures in the first study phase, and an individualized algorithm in the second study phase. Caregivers completed a quality of life (QoL) questionnaire at the initial and final study visit. RESULTS:Using the predefined algorithm, the accelerometer detected seizures with a sensitivity of 18.6% (95% CI [13.4%, 23.8%]) and mean false detection rate of 0.096/day. Values did not change significantly with use of an individualized algorithm (sensitivity 22.1%, 95% CI [15.1%, 29.0%]; false detection rate 0.054/day). Mean composite QoL score was significantly improved at study completion (50.42) compared to study initiation (39.53; P =?.005), and this change was moderately correlated with a change in weekly exercise (r =?0.46, P =?.05). CONCLUSIONS AND CLINICAL IMPORTANCE:Generalized seizures in dogs can be detected with a collar-mounted accelerometer, but the overall sensitivity is low.
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Project description:Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices' position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.
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Project description:ObjectivesRecovery of independent ambulation after stroke is a major goal. However, which rehabilitation regimen best benefits each individual is unknown and decisions are currently made on a subjective basis. Predictors of response to specific therapies would guide the type of therapy most appropriate for each patient. Although lesion topography is a strong predictor of upper limb response, walking involves more distributed functions. Earlier studies that assessed the cortico-spinal tract (CST) were negative, suggesting other structures may be important.Experimental designThe relationship between lesion topography and response of walking speed to standard rehabilitation was assessed in 50 adult-onset patients using both volumetric measurement of CST lesion load and voxel-based lesion-symptom mapping (VLSM) to assess non-CST structures. Two functional mobility scales, the functional ambulation category (FAC) and the modified rivermead mobility index (MRMI) were also administered. Performance measures were obtained both at entry into the study (3-42 days post-stroke) and at the end of a 6-week course of therapy. Baseline score, age, time since stroke onset and white matter hyperintensities score were included as nuisance covariates in regression models.Principal observationsCST damage independently predicted response to therapy for FAC and MRMI, but not for walk speed. However, using VLSM the latter was predicted by damage to the putamen, insula, external capsule and neighbouring white matter.ConclusionsWalk speed response to rehabilitation was affected by damage involving the putamen and neighbouring structures but not the CST, while the latter had modest but significant impact on everyday functions of general mobility and gait. Hum Brain Mapp 37:689-703, 2016. © 2015 Wiley Periodicals, Inc.
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Project description:Traditional calibration method is usually performed with expensive equipments suchas three-axis turntable in a laboratory environment. However in practice, in order to ensure theaccuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibratethe inertial measurement unit (IMU) without external equipment in the field. In this paper, anew in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search(BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithmand its improvements based on basic beetle antennae search (BAS) algorithm are introduced indetail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established forhigher calibration accuracy, and then 24 optimal measurement positions are designed by theoreticalanalysis. In addition, the calibration procedures are improved according to the characteristics of BSASalgorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm.Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, whichshows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate thatthe proposed method can achieve high precision in-field calibration without any external equipment,and meet the accuracy requirements of the INS.
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Project description:The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named "ShimFall&ADL" will be publicly released to facilitate further research on the field.
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Project description:Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.
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Project description:Gait speed is a measure of general fitness. Changing from usual (UGS) to maximum (MGS) gait speed requires coordinated action of many body systems. Gait speed reserve (GSR) is defined as MGS-UGS. From a shortlist of 88 features across five categories including sociodemographic, cognitive, and physiological, we aimed to find and compare the sets of predictors that best describe UGS, MGS, and GSR. For this, we leveraged data from 3,925 adults aged 50+ from Wave 3 of The Irish Longitudinal Study on Ageing (TILDA). Features were selected by a histogram gradient boosting regression-based stepwise feature selection pipeline. Each model's feature importance and input-output relationships were explored using TreeExplainer from the Shapely Additive Explanations explainable machine learning package. The mean Radj2 (SD) from fivefold cross-validation on training data and the Radj2 score on test data were 0.38 (0.04) and 0.41 for UGS, 0.45 (0.04) and 0.46 for MGS, and 0.19 (0.02) and 0.21 for GSR. Each model selected features across all categories. Features common to all models were age, grip strength, chair stands time, mean motor reaction time, and height. Exclusive to UGS and MGS were educational attainment, fear of falling, Montreal cognitive assessment errors, and orthostatic intolerance. Exclusive to MGS and GSR were body mass index (BMI), and number of medications. No features were selected exclusively for UGS and GSR. Features unique to UGS were resting-state pulse interval, Center for Epidemiologic Studies Depression Scale (CESD) depression, sit-to-stand difference in diastolic blood pressure, and left visual acuity. Unique to MGS were standard deviation in sustained attention to response task times, resting-state heart rate, smoking status, total heartbeat power during paced breathing, and visual acuity. Unique to GSR were accuracy proportion in a sound-induced flash illusion test, Mini-mental State Examination errors, and number of cardiovascular conditions. No interactions were present in the GSR model. The four features that overall gave the most impactful interactions in the UGS and MGS models were age, chair stands time, grip strength, and BMI. These findings may help provide new insights into the multisystem predictors of gait speed and gait speed reserve in older adults and support a network physiology approach to their study.
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Project description:Golden Retriever Muscular Dystrophy (GRMD) is a dystrophin-deficient canine model genetically homologous to Duchenne Muscular Dystrophy (DMD) in humans. Muscular fibrosis secondary to cycles of degeneration/regeneration of dystrophic muscle tissue and muscular weakness leads to biomechanical adaptation that impairs the quality of gait. Physical therapy (PT) is one of the supportive therapies available for DMD, however, motor PT approaches have controversial recommendations and there is no consensus regarding the type and intensity of physical therapy. In this study we investigated the effect of physical therapy on gait biomechanics and muscular collagen deposition types I and III in dystrophin-deficient dogs. Two dystrophic dogs (treated dogs-TD) underwent a PT protocol of active walking exercise, 3×/week, 40 minutes/day, 12 weeks. Two dystrophic control dogs (CD) maintained their routine of activities of daily living. At t0 (pre) and t1 (post-physical therapy), collagen type I and III were assessed by immunohistochemistry and gait biomechanics were analyzed. Angular displacement of shoulder, elbow, carpal, hip, stifle and tarsal joint and vertical (Fy), mediolateral (Fz) and craniocaudal (Fx) ground reaction forces (GRF) were assessed. Wilcoxon test was used to verify the difference of biomechanical variables between t0 and t1, considering p<.05. Type I collagen of endomysium suffered the influence of PT, as well as gait speed that had decreased from t0 to t1 (p<.000). The PT protocol employed accelerates morphological alterations on dystrophic muscle and promotes a slower velocity of gait. Control dogs which maintained their routine of activities of daily living seem to have found a better balance between movement and preservation of motor function.
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Project description:The purpose of this study was to establish GENEA (Gravity Estimator of Normal Everyday Activity) cut-points for discriminating between six relative-intensity activity levels in middle-aged recreational marathoners. Nighty-eight (83 males and 15 females) recreational marathoners, aged 30-45 years, completed a cardiopulmonary exercise test running on a treadmill while wearing a GENEA accelerometer on their non-dominant wrist. The breath-by-breath V̇O2 data was also collected for criterion measure of physical activity categories (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). GENEA cut-points for physical activity classification was performed via Receiver Operating Characteristic (ROC) analysis. Spearman's correlation test was applied to determine the relationship between estimated and measured intensity classifications. Statistical analysis were done for all individuals, and separating samples by sex. The GENEA cut-points established were able to distinguish between all six-relative intensity levels with an excellent classification accuracy (area under the ROC curve (AUC) values between 0.886 and 0.973) for all samples. When samples were separated by sex, AUC values were 0.881-0.973 and 0.924-0.968 for males and females, respectively. The total variance in energy expenditure explained by GENEA accelerometer data was 78.50% for all samples, 78.14% for males, and 83.17% for females. In conclusion, the wrist-worn GENEA accelerometer presents a high capacity of classifying the intensity of physical activity in middle-aged recreational marathoners when examining all samples together, as well as when sample set was separated by sex. This study suggests that the triaxial GENEA accelerometers (worn on the non-dominant wrist) can be used to predict energy expenditure for running activities.
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