GaitKeeper: a system for measuring canine gait

It is understood gait has the potential to be used as a window into neurodegenerative disorders, identify markers of subclinical pathology, inform diagnostic algorithms of disease progression and measure the efficacy of interventions. Dogs’ gaits are frequently assessed in a veterinary setting to detect signs of lameness. Despite this, a reliable, affordable and objective method to assess lameness in dogs is lacking. Most described canine lameness assessments are subjective, unvalidated and at high risk of bias. This means reliable, early detection of canine gait abnormalities is challenging, which may have detrimental implications for dogs’ welfare. In this paper, we draw from approaches and technologies used in human movement science and describe a system for objectively measuring temporal gait characteristics in dogs (step-time, swing-time, stance-time). Asymmetries and variabilities in these characteristics are of known clinical significance when assessing lameness but presently may only be assessed on coarse scales or under highly instrumented environments. The system consists an inertial measurement unit, containing a 3-axis accelerometer and gyroscope coupled with a standardized walking course. The measurement unit is attached to each leg of the dog under assessment before it is walked around the course. The data by the measurement unit is then processed to identify steps and subsequently, micro-gait characteristics. This method has been tested on a cohort of 19 healthy dogs of various breeds ranging in height from 34.2 cm to 84.9 cm. We report the system as capable of making precise step delineations with detections of initial and final contact times of foot-to-floor to a mean precision of 0.011 s and 0.048 s, respectively. Results are based on analysis of 12,678 foot falls and we report a sensitivity, positive predictive value and F-score of 0.81, 0.83 and 0.82 respectively. To investigate the effect of gait on system performance, the approach was tested in both walking and trotting with no significant performance deviation with 7249 steps reported for a walking gait and 4977 for a trotting gait. The number of steps reported for each leg were approximately equal and this consistency was true in both walking and trotting gaits. In the walking gait 1965, 1790, 1726 and 1768 steps were reported for the front left, front right, hind left and hind right legs respectively. 1361, 1250, 1176 and 1190 steps were reported for each of the four legs in the trotting gait. The proposed system is a pragmatic and precise solution for obtaining objective measurements of canine gait. With further development, it promises potential for a wide range of applications in both research and clinical practice

The ambient kitchen: a pervasive sensing environment for situated services

In this paper we describe the demonstration of the Ambient Kitchen, a pervasive sensing environment designed for improving cooking skills, promoting healthier eating, and helping cognitively impaired people to live more independent in their own homes. The kitchen is instrumented with an embedded sensing infrastructure including RFID, Newcastle University Culture lab’s proprietary wireless accelerometers (WAX), microphone, camera, pressure sensors and tablet computers. Several applications including real-time activity recognition, recipe displays, and real-time food recognition are deployed in our kitchen

Pervasive sensing as a mechanism for the effective control of CHP plant in commercial buildings

A recently completed, EPSRC-funded project researched the use of low cost, pervasive sensing to monitor building environmental conditions and occupant interactions as a means to reduce the uncertainties associated with the creation of a building model for refurbishment options and smarter control appraisal. This paper gives a brief introduction to the pervasive sensing system as established within the project and describes its use to enable simulations of the multi-input, multi-output (MIMO) control of a combined heat and power (CHP) unit in a commercial building context. Within the project, data from pervasive sensing was used to calibrate a simulation model of an office building and impose occupant-related inputs at the time step level as a means to reduce modelling uncertainty. The MIMO input parameters considered include space temperatures, heat store temperatures, electricity demand and electricity tariff, while the output parameters include space heat supply, heat stored, electricity utilised locally or exported, and CHP unit fuel use. The simulation model was used to compare performance when the CHP unit is subjected to conventional and MIMO control. It is demonstrated that the pervasive sensing approach enables control that delivers enhanced energy performance

An open-label randomised clinical trial to compare the efficacy of dietary caloric restriction and physical activity for weight loss in overweight pet dogs

Canine obesity is usually managed with a combination of dietary caloric restriction and increasing physical activity, but no previous study has compared both of these strategies in a prospective randomised controlled trial. Thirteen overweight dogs (body condition score 6–9/9) were randomised to one of two interventions: dietary caloric restriction or physical activity. The dietary caloric restriction intervention comprised feeding a therapeutic weight loss diet, while the physical activity intervention comprised increasing the dog’s current physical activity pattern by at least a third. The primary outcome measure was change in body weight, while secondary outcome measures included change in neck, thorax and abdominal circumference and change in physical activity measured by triaxial accelerometer. Bodyweight decreased significantly with the dietary caloric restriction (median −10% of starting body weight [SBW], 5 to −12%; P = 0.028) but not with the physical activity intervention (−2% SBW, +3% to −6%; P = 0.107). Abdominal circumference (dietary caloric restriction: median −12.0%; physical activity: median −7.8%, P = 0.016) and thoracic circumference (dietary caloric restriction: median −7.5%, P = 0.031; physical activity: median −3.6%, P = 0.031) changed significantly in both groups. There was no change in activity levels within the dietary caloric restriction group, but vigorous activity increased significantly in the physical activity group (P = 0.016). Dietary caloric restriction was more effective than physical activity for controlled weight loss in overweight pet dogs. Although advising owners to increase their dog’s activity by a third led to a modest increase in measured vigorous physical activity, this was insufficient to promote weight loss on its own

Pervasive sensing as a mechanicsm for the effective control of CHP plant in commerical buildings

A recently completed, EPSRC-funded project researched the use of low cost, pervasive sensing to monitor building environmental conditions and occupant interactions as a means to reduce the uncertainties associated with the creation of a building model for refurbishment options and smarter control appraisal. This paper gives a brief introduction to the pervasive sensing system as established within the project and describes its use to enable simulations of the multi-input, multi-output (MIMO) control of a combined heat and power (CHP) unit in a commercial building context. Within the project, data from pervasive sensing was used to calibrate a simulation model of an office building and impose occupant-related inputs at the time step level as a means to reduce modelling uncertainty. The MIMO input parameters considered include space temperatures, heat store temperatures, electricity demand and electricity tariff, while the output parameters include space heat supply, heat stored, electricity utilised locally or exported, and CHP unit fuel use. The simulation model was used to compare performance when the CHP unit is subjected to conventional and MIMO control. It is demonstrated that the pervasive sensing approach enables control that delivers enhanced energy performance

Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework

Saving energy in residential and commercial buildings is of great interest due to diminishing resources. Heating ventilation and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method. We integrate potentially uncertain contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low-and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving

GaitKeeper: A System for Measuring Canine Gait

It is understood gait has the potential to be used as a window into neurodegenerative disorders, identify markers of subclinical pathology, inform diagnostic algorithms of disease progression and measure the efficacy of interventions. Dogs’ gaits are frequently assessed in a veterinary setting to detect signs of lameness. Despite this, a reliable, affordable and objective method to assess lameness in dogs is lacking. Most described canine lameness assessments are subjective, unvalidated and at high risk of bias. This means reliable, early detection of canine gait abnormalities is challenging, which may have detrimental implications for dogs’ welfare. In this paper, we draw from approaches and technologies used in human movement science and describe a system for objectively measuring temporal gait characteristics in dogs (step-time, swing-time, stance-time). Asymmetries and variabilities in these characteristics are of known clinical significance when assessing lameness but presently may only be assessed on coarse scales or under highly instrumented environments. The system consists an inertial measurement unit, containing a 3-axis accelerometer and gyroscope coupled with a standardized walking course. The measurement unit is attached to each leg of the dog under assessment before it is walked around the course. The data by the measurement unit is then processed to identify steps and subsequently, micro-gait characteristics. This method has been tested on a cohort of 19 healthy dogs of various breeds ranging in height from 34.2 cm to 84.9 cm. We report the system as capable of making precise step delineations with detections of initial and final contact times of foot-to-floor to a mean precision of 0.011 s and 0.048 s, respectively. Results are based on analysis of 12,678 foot falls and we report a sensitivity, positive predictive value and F-score of 0.81, 0.83 and 0.82 respectively. To investigate the effect of gait on system performance, the approach was tested in both walking and trotting with no significant performance deviation with 7249 steps reported for a walking gait and 4977 for a trotting gait. The number of steps reported for each leg were approximately equal and this consistency was true in both walking and trotting gaits. In the walking gait 1965, 1790, 1726 and 1768 steps were reported for the front left, front right, hind left and hind right legs respectively. 1361, 1250, 1176 and 1190 steps were reported for each of the four legs in the trotting gait. The proposed system is a pragmatic and precise solution for obtaining objective measurements of canine gait. With further development, it promises potential for a wide range of applications in both research and clinical practice

Positioning Algorithms for Indoor Wireless Sensor Networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Combined Approach to Predicting Rest in Dogs Using Accelerometers

The ability to objectively measure episodes of rest has clear application for assessing health and well-being. Accelerometers afford a sensitive platform for doing so and have demonstrated their use in many human-based trials and interventions. Current state of the art methods for predicting sleep from accelerometer signals are either based on posture or low movement. While both have proven to be sensitive in humans, the methods do not directly transfer well to dogs, possibly because dogs are commonly alert but physically inactive when recumbent. In this paper, we combine a previously validated low-movement algorithm developed for humans and a posture-based algorithm developed for dogs. The hybrid approach was tested on 12 healthy dogs of varying breeds and sizes in their homes. The approach predicted state of rest with a mean accuracy of 0.86 (SD = 0.08). Furthermore, when a dog was in a resting state, the method was able to distinguish between head up and head down posture with a mean accuracy of 0.90 (SD = 0.08). This approach can be applied in a variety of contexts to assess how factors, such as changes in housing conditions or medication, may influence a dog’s resting patterns

Toward a low-cost gait analysis system for clinical and free-living assessment

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to bespoke clinical facilities until recently. The use of inexpensive wearable technologies is an attractive alternative and offers the potential to assess gait in any environment. In this paper we present the development of a low cost analysis gait system built using entirely open source components. The system is used to capture spatio-temporal gait characteristics derived from an existing conceptual model, sensitive to ageing and neurodegenerative pathology (e.g. Parkinson's disease). We demonstrate the system is suitable for use in a clinical unit and will lead to pragmatic use in a free-living (home) environment. The system consists of a wearable (tri-axial accelerometer and gyroscope) with a Raspberry Pi module for data storage and analysis. This forms ongoing work to develop gait as a low cost diagnostic in modern healthcare