Potential use of actigraphy to measure sleep in monkeys: comparison with behavioral analysis from videography.

Sleep is indispensable for human health, with sleep disorders initiating a cascade of negative consequences. As our closest phylogenetic relatives, non-human primates (NHPs) are invaluable for comparative sleep studies and exhibit tremendous potential for improving our understanding of human sleep and related disorders. Previous work on measuring sleep in NHPs has mostly used electroencephalography or videography. In this study, simultaneous videography and actigraphy were applied to observe sleep patterns in 10 cynomolgus monkeys ( Macaca fascicularis) over seven nights (12 h per night). The durations of wake, transitional sleep, and relaxed sleep were scored by analysis of animal behaviors from videography and actigraphy data, using the same behavioral criteria for each state, with findings then compared. Here, results indicated that actigraphy constituted a reliable approach for scoring the state of sleep in monkeys and showed a significant correlation with that scored by videography. Epoch-by-epoch analysis further indicated that actigraphy was more suitable for scoring the state of relaxed sleep, correctly identifying 97.57% of relaxed sleep in comparison with video analysis. Only 34 epochs (0.13%) and 611 epochs (2.30%) were differently interpreted as wake and transitional sleep compared with videography analysis. The present study validated the behavioral criteria and actigraphy methodology for scoring sleep, which can be considered as a useful and a complementary technique to electroencephalography and/or videography analysis for sleep studies in NHPs.

Novel use of an activity monitor to model jumping behaviors in cats.

OBJECTIVE: To develop methods to identify and characterize activity monitor (AM) data signatures for jumps performed by cats. ANIMALS: 13 healthy, client-owned cats without evidence of osteoarthritis or degenerative joint disease. PROCEDURES: Each cat was fitted with the same AM, individually placed in an observation room, then simultaneously recorded by 3 video cameras during the observation period (5 to 8 hours). Each cat was encouraged to jump up (JU), jump down (JD), and jump across (JA) during the observation period. Output from the AM was manually annotated for jumping events, each of which was characterized by functional data analysis yielding relevant coefficients. The coefficients were then used in linear discriminant analysis to differentiate recorded jumps as JUs, JDs, or JAs. To assess the model's ability to distinguish among the 3 jump types, a leave-one-out cross-validation method was used, and the misclassification error rate of the overall categorization of the model was calculated. RESULTS: Of 731 jumping events, 29 were misclassified. Overall, the mean misclassification error rate per cat was 5.4% (range, 0% to 12.5%), conversely indicating a correct classification rate per cat of 94.6%. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that the model was successful in correctly identifying JUs, JDs, and JAs in healthy cats. With advancements in AM technology and data processing, there is potential for the model to be applied in clinical settings as a means to obtain objective outcome measures.

Initial evaluation of PetPace activity monitor.

Mobility is considered a vital component of health and quality of life in humans and companion animals. Wearable devices for pets that can monitor activity and other aspects of health are increasingly being marketed to veterinarians and owners, with claims around their ability to monitor aspects of health. However, there is little scientific evidence to support the validity of these claims. To address this, the objective of this study was to assess the correlation of the activity measurement from the PetPace device compared to activity output from Actigraph and the validated Actical device. Ten client-owned, healthy dogs were used for the study. The three devices were mounted simultaneously on a dedicated collar and activity was recorded during a period of 7 days. There were moderate correlations between the Actical and the PetPace (r2=0.59, P=<0.001). There was high correlation between the PetPace and the Actigraph (r2=0.85, P=<0.001) and between the Actical and the Actigraph (r2=0.72, P=<0.001). If the Actical activity counts were limited under 50,000 per hour, there was strong correlation between the Actical and the PetPace (r2=0.71, P=<0.001) and between the Actical and the Actigraph (r2=0.86, P=<0.001). PetPace has a moderate correlation with the most validated activity monitor that has been used in veterinary medicine. Its real-time data acquisition, user friendly interface for owners and cost make this device an attractive tool for monitoring activity in dogs. Further studies maybe needed to evaluate its performance, validity and clinical utility in the field.

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential.

Development and validation of a novel pedometer algorithm to quantify extended characteristics of the locomotor behavior of dairy cows.

Behavior is one of the most important indicators for assessing cattle health and well-being. The objective of this study was to develop and validate a novel algorithm to monitor locomotor behavior of loose-housed dairy cows based on the output of the RumiWatch pedometer (ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Data of locomotion were acquired by simultaneous pedometer measurements at a sampling rate of 10 Hz and video recordings for manual observation later. The study consisted of 3 independent experiments. Experiment 1 was carried out to develop and validate the algorithm for lying behavior, experiment 2 for walking and standing behavior, and experiment 3 for stride duration and stride length. The final version was validated, using the raw data, collected from cows not included in the development of the algorithm. Spearman correlation coefficients were calculated between accelerometer variables and respective data derived from the video recordings (gold standard). Dichotomous data were expressed as the proportion of correctly detected events, and the overall difference for continuous data was expressed as the relative measurement error. The proportions for correctly detected events or bouts were 1 for stand ups, lie downs, standing bouts, and lying bouts and 0.99 for walking bouts. The relative measurement error and Spearman correlation coefficient for lying time were 0.09% and 1; for standing time, 4.7% and 0.96; for walking time, 17.12% and 0.96; for number of strides, 6.23% and 0.98; for stride duration, 6.65% and 0.75; and for stride length, 11.92% and 0.81, respectively. The strong to very high correlations of the variables between visual observation and converted pedometer data indicate that the novel RumiWatch algorithm may markedly improve automated livestock management systems for efficient health monitoring of dairy cows.

A 6-month observational study of changes in objectively measured physical activity during weight loss in dogs.

OBJECTIVES: To evaluate long-term changes in physical activity and sedentary behaviour during weight loss in dogs. METHODS: Sixteen overweight and obese dogs undergoing a 6-month calorie-controlled weight-loss programme wore Actigraph GT3X accelerometers (Actigraph, FL) for three consecutive days each month for the duration of the programme. Total volume of physical activity and time spent in sedentary behaviour, light-moderate intensity physical activity and vigorous intensity physical activity were extracted from the accelerometer data and compared from baseline to month 6. RESULTS: Valid accelerometry data were returned for 14 of 16 dogs. Mean percentage weight loss over 6 months was 15% of initial bodyweight. There was no marked increase in any of the physical activity outcomes or reduction in sedentary behaviour. CLINICAL SIGNIFICANCE: Substantial weight loss was not associated with a spontaneous increase in physical activity or reduction in sedentary behaviour. Although the dogs in this study lost a substantial amount of bodyweight without a measured increase in physical activity, dog owners should still be encouraged to facilitate increased physical activity in their dogs owing to the wide range of benefits associated with physical activity other than weight loss.

Associations between obesity and physical activity in dogs: a preliminary investigation.

OBJECTIVES: To assess whether obesity has any association with objectively measured physical activity levels in dogs. METHODS: Thirty-nine dogs wore Actigraph GT3X accelerometers (Actigraph) for 7 consecutive days. Each dog was classified as ideal weight, overweight or obese using the 5-point body condition scoring system. Total volume of physical activity and time spent in sedentary behaviour, light-moderate intensity physical activity and vigorous intensity physical activity were compared between body condition categories. RESULTS: Valid accelerometry data were returned for 35 of 39 dogs recruited. Eighteen dogs were classed as ideal weight, 9 as overweight and the remaining 8 as obese. All dogs spent a significant proportion of the day sedentary and obese dogs spent significantly less time in vigorous intensity physical activity than ideal weight dogs (7 ±3 minute/day versus 21 ±15 minute/day, P=0·01). CLINICAL SIGNIFICANCE: Obesity is associated with lower vigorous intensity physical activity in dogs, as is also thought to occur in humans. These preliminary findings will help inform a future, larger study and may also improve our understanding of the associations between obesity and physical activity in dogs.

Primate drum kit: a system for studying acoustic pattern production by non-human primates using acceleration and strain sensors.

The possibility of achieving experimentally controlled, non-vocal acoustic production in non-human primates is a key step to enable the testing of a number of hypotheses on primate behavior and cognition. However, no device or solution is currently available, with the use of sensors in non-human animals being almost exclusively devoted to applications in food industry and animal surveillance. Specifically, no device exists which simultaneously allows: (i) spontaneous production of sound or music by non-human animals via object manipulation, (ii) systematical recording of data sensed from these movements, (iii) the possibility to alter the acoustic feedback properties of the object using remote control. We present two prototypes we developed for application with chimpanzees (Pan troglodytes) which, while fulfilling the aforementioned requirements, allow to arbitrarily associate sounds to physical object movements. The prototypes differ in sensing technology, costs, intended use and construction requirements. One prototype uses four piezoelectric elements embedded between layers of Plexiglas and foam. Strain data is sent to a computer running Python through an Arduino board. A second prototype consists in a modified Wii Remote contained in a gum toy. Acceleration data is sent via Bluetooth to a computer running Max/MSP. We successfully pilot tested the first device with a group of chimpanzees. We foresee using these devices for a range of cognitive experiments.

Gait analysis using 3D accelerometry in horses sedated with xylazine.

The aim of the present study was to verify the efficacy and sensitivity of an accelerometric device in detecting and quantifying the degree of movement alteration produced in horses sedated with xylazine. Horses (n=6) were randomly administered either xylazine or a control by intravenous injection, with at least 1 week between each treatment. A triaxial accelerometric device was used for the accelerometric gait assessment 15 min before (baseline) and 5, 15, 30, 45, 60, 75, 90, 105 and 120 min after each treatment. Eight different accelerometric parameters were calculated, including speed, stride frequency, stride length, regularity, dorsoventral power, propulsion power, mediolateral power and total power, with the force of acceleration and the dorsoventral, mediolateral and craniocaudal (propulsive) parts of the power then calculated. Administration of xylazine decreased many of the parameters investigated, with significant differences for speed, stride frequency, dorsoventral power, propulsion power and total power at 5, 15, 30 and 45 min after injection. There were no significant differences in stride length values at any time point. Decreases in regularity values were evident with significant differences at every time point from 5 to 120 min following xylazine injection. Force values were also significantly reduced from 5 to 30 min after treatment and a redistribution of the total power was observed 5 min after injection as the mediolateral power increased significantly, while the dorsoventral power decreased. Accelerometry offers a practical, accurate, easy to use, portable and low cost method of objectively monitoring gait abnormalities at the walk in horses after sedation with xylazine.