For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.

A prolonged break-over phase might be an indication of a variety of musculoskeletal disorders and can be measured with optical motion capture (OMC) systems, inertial measurement units (IMUs) and force plates. The aim of this study was to present two algorithms for automatic detection of the break-over phase onset from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. The performance of these algorithms was evaluated by internal validation with an OMC system and a force plate separately. Seven Warmblood horses were equipped with two wireless IMUs which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted over a force plate for internal validation while simultaneously the 3D position of three reflective markers, attached to lateral heel, lateral toe and lateral coronet of each hoof, were measured by six infrared cameras of an OMC system. The performance of the algorithms was evaluated by linear mixed model analysis. The acceleration algorithm was the most accurate with an accuracy between -9 and 23 ms and a precision around 24 ms (against OMC system), and an accuracy between -37 and 20 ms and a precision around 29 ms (against force plate), depending on gait and hoof. This algorithm seems promising for quantification of the break-over phase onset although the applicability for clinical purposes, such as lameness detection and evaluation of trimming and shoeing techniques, should be investigated more in-depth.

A noncontact mapping system (EnSite) was used for electroanatomical mapping of the bladder simultaneously with pressure flow study in three women with lower urinary tract symptoms. We selected the periods of obvious detrusor activity. Data were processed to remove baseline drift, and an envelope of electrovesicography (EVG) data was created. The correlation coefficient for the correlation between between the EVG envelope and the detrusor pressure (Pdet) was calculated. Bladder geometry was successfully created in all 3 patients. Simultaneous recording of EVG and pressure flow data was successful in 1 patient. Scatter plots were made of the highest correlation coefficient, showing a positive correlation between the Pdet and the envelope, and negative correlation between abdominal pressure (Pabd) and the envelope. Minimal electrical activity could be observed. Significant weak to moderate correlation coefficients were found for the correlations between Pdet and EVG and between Pabd and EVG.

Background: Polysomnography (PSG) is the gold standard for diagnosing and monitoring sleep disorders, however, it is time-consuming and costly, as the application of the equipment can only be done by trained sleep technicians and the test must be conducted within a sleep laboratory. Objectives: In this study we assessed the performance of the first wireless patch-based PSG system, the Onera Sleep Test System (STS), which can be applied by the patient and performed outside of the sleep laboratory in settings such as the home. To achieve this, sleep stage and physiological data from the Onera STS were compared to gold standard in-lab PSG. Materials and methods: The recordings were unsupervised to simulate a home-use environment. Epoch-by-epoch agreement was assessed by calculating sensitivity, specificity, accuracy, and Cohens kappa coefficient. Pearsons correlation coefficients were calculated for multiple sleep parameters to measure the level of entire night agreement. Results: Substantial agreement with a Cohens kappa of 0.69 across all sleep stages was determined, which reached 0.81 when Stage N1 was removed from the analysis. A high accuracy, specificity, and sensitivity were found for wake N2, N3 and REM. Although specificity (95.25%) and accuracy (89.62%) were high for N1, sensitivity was low (27.19%). Sleep parameters calculated by sleep stage transitions, apnea hypopnea index and oxygen desaturation index, showed strong correlations. Conclusions: The Onera STS provides comparable clinical information to traditional PSG. Moreover, the application time was reduced by 77% which reduces the overall costs of PSG. These results open the possibility for PSG studies to be performed efficiently outside of the sleep laboratory at a larger scale, thus improving access for patients.

The aim of this study is to describe the kinematic gait characteristics of straight line walk in clinically sound dairy cows using body mounted Inertial Measurement Units (IMUs) at multiple anatomical locations. The temporal parameters used are speed and non-speed normalized stance duration, bipedal and tripedal support durations, maximal protraction and retraction angles of the distal limbs and vertical displacement curves of the upper body. Gait analysis was performed by letting 17 dairy cows walk in a straight line at their own chosen pace while equipped with IMU sensors on tubera sacrale, left and right tuber coxae (LTC and RTC), back, withers, head, neck and all four lower limbs. Data intervals with stride by stride regularity were selected based on video data. For temporal parameters, the median was calculated and 95% confidence intervals (CI) were estimated based on linear mixed model (LMM) analysis, while for limb and vertical displacement curves, the median and most typical curves were calculated. The temporal parameters and distal limb angles showed consistent results with low variance and LMM analysis showed non-overlapping CI for all temporal parameters. The distal limb angle curves showed a larger and steeper retraction angle range for the distal front limbs compared with the hind limbs. The vertical displacement curves of the sacrum, withers, LTC and RTC showed a consistent sinusoidal pattern while the head, back and collar curves were less consistent and showed more variation between and within cows. This kinematic description might allow to objectively differentiate between normal and lame gait in the future and determine the best anatomical location for sensor attachment for lameness detection purposes.