Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition

Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity

Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

Background Classifying behaviour with animal-borne accelerometers is quickly becoming a popular tool for remotely observing behavioural states in a variety of species. Most accelerometry work in pinnipeds has focused on classifying behaviour at sea often quantifying behavioural trade-offs associated with foraging and diving in income breeders. Very little work to date has been done to resolve behaviour during the critical period of lactation in a capital breeder. Capital breeding phocids possess finite reserves that they must allocate appropriately to maintain themselves and their new offspring during their brief nursing period. Within this short time, fine-scale behavioural trade-offs can have significant fitness consequences for mother and offspring and must be carefully managed. Here, we present a case study in extracting and classifying lactation behaviours in a wild, breeding pinniped, the grey seal (Halichoerus grypus). Results Using random forest models, we were able to resolve 4 behavioural states that constitute the majority of a female grey seals’ activity budget during lactation. Resting, alert, nursing, and a form of pup interaction were extracted and classified reliably. For the first time, we quantified the potential confounding variance associated with individual differences in a wild context as well as differences due to sampling location in a largely inactive model species. Conclusions At this stage, the majority of a female grey seal’s activity budget was classified well using accelerometers, but some rare and context-dependent behaviours were not well captured. While we did find significant variation between individuals in behavioural mechanics, individuals did not differ significantly within themselves; inter-individual variability should be an important consideration in future efforts. These methods can be extended to other efforts to study grey seals and other pinnipeds who exhibit a capital breeding system. Using accelerometers to classify behaviour during lactation allows for fine-scale assessments of time and energy trade-offs for species with fixed stores

Quantifying individual variation in fine-scale time and energy trade-offs in breeding grey seals: How do differing behavioural types solve these trade-offs?

Lactation is one of the most energetically demanding periods of any female mammal’s life history, where individuals strike a balance with limited resources between their daily activity and towards the growth of their offspring, while still maintaining enough energy stores to maintain themselves in the process. Capital breeding systems mean that females must sustain themselves and their offspring while fasting exclusively on energy reserves acquired beforehand. Female phocids as a result must deal with pressures of a brief terrestrial existence where trade-offs in time, behaviour, energy, and responsiveness to the environment can have tangible consequences to short-term fitness and health. The aim of this thesis was to use new techniques, specifically animal-borne accelerometers and heart rate monitors, to track behaviour and physiology and assess the inherent trade-offs therein through the core duration of lactation in a capital breeding phocid, the grey seal (Halichoerus grypus). Female grey seals were equipped with biologging devices on the Isle of May over three consecutive breeding seasons. Using accelerometry and heart rate techniques, I aimed (1) to remotely classify behaviour using machine learning techniques, (2) to assess trade-offs in time-activity for the duration of lactation, (3) to build a holistic picture of energy allocation within the species, and (4) to develop new methods for tracking heart rate and breathing for terrestrial mammals using grey seals as a model. I also assessed the effect that consistent individual variability in behaviour, stress-coping styles, may have on the methods developed here and how they may drive behaviour and energy trade-offs over time. Accelerometers presented a useful way to remotely track several key behaviours, accurately classifying the core static behaviours over lactation. Consistent individual differences in stress-coping styles, as determined from measures of heart rate variability, modulated almost every aspect of behaviour and physiology measured in this study. More specifically, consistent trade-offs were identified for grey seal mothers between balancing time spent in a state of rest against remaining vigilant across multiple contexts, but also that these individual differences drove how individuals manage and expend that energy, ultimately resulting in differences in short-term fitness outcomes. Effort towards nursing, however, appeared to be largely fixed. Individual differences in energy management also appear to result in different levels of plasticity to environmental pressures, suggesting that future ambient conditions may not be suitable for breeding seals. This thesis also successfully detected breathing rates on land, revealing new evidence as to the energy saving and water conservation benefits of regularly engaging in periods of breath-hold while at rest. Overall, this thesis has provided new tools for exploring behaviour and physiology, and the inherent trade-offs therein, with minimal disturbance to lactating phocid seals. These differences, while minute in the scope of evolutionary constraints, may be among the most important drivers for the success and survival of populations in the face of greater environmental variability as the climate continues to change

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Quantifying the swimming gaits of veined squid (Loligo forbesi) using bio-logging tags

Author Posting. © Company of Biologists, 2019. This article is posted here by permission of Company of Biologists for personal use, not for redistribution. The definitive version was published in Journal of Experimental Biology 222 (2019):jeb.198226, doi: 10.1242/jeb.198226.Squid are mobile, diverse, ecologically important marine organisms whose behavior and habitat use can have substantial impacts on ecosystems and fisheries. However, as a consequence in part of the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these free-swimming, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Here, we applied a novel, high-resolution bio-logging tag (ITAG) to seven veined squid, Loligo forbesii, in a controlled experimental environment to quantify their short-term (24 h) behavioral patterns. Tag accelerometer, magnetometer and pressure data were used to develop automated gait classification algorithms based on overall dynamic body acceleration, and a subset of the events were assessed and confirmed using concurrently collected video data. Finning, flapping and jetting gaits were observed, with the low-acceleration finning gaits detected most often. The animals routinely used a finning gait to ascend (climb) and then glide during descent with fins extended in the tank's water column, a possible strategy to improve swimming efficiency for these negatively buoyant animals. Arms- and mantle-first directional swimming were observed in approximately equal proportions, and the squid were slightly but significantly more active at night. These tag-based observations are novel for squid and indicate a more efficient mode of movement than suggested by some previous observations. The combination of sensing, classification and estimation developed and applied here will enable the quantification of squid activity patterns in the wild to provide new biological information, such as in situ identification of behavioral states, temporal patterns, habitat requirements, energy expenditure and interactions of squid through space–time in the wild.This work was supported by Woods Hole Oceanographic Institution’s Ocean Life Institute and the Innovative Technology Program, Hopkins Marine Station’s Marine Life Observatory (to K.K.), as well as the National Science Foundation Program for Instrument Development for Biological Research (award no. 1455593 to T.A.M., K.K. and K.A.S.). F.C. thanks the Presidentís International Fellowship Initiative (PIFI) of the Chinese Academy of Science. G.E.F. thanks the National Science Foundation GRFP and National Science Foundation REU programs for support of this research.2020-10-2

Detecting Feeding and Estimating the Energetic Costs of Diving in California Sea Lions (Zalophus californianus) Using 3-Axis Accelerometers

Knowledge of when animals feed and the energetic costs of foraging is key to understanding their foraging ecology and energetic trade-offs. Despite this importance, our ability to collect these data in marine mammals remains limited. In this thesis, I address knowledge gaps in both feeding detection and fine-scale diving energetic costs in a model species, the California sea lion (Zalophus californianus). I first developed and tested an analysis method to accurately detect prey capture using 3-axis accelerometers mounted on the head and back of two trained sea lions. An acceleration signal pattern isolated from a ‘training’ subset of synced video and acceleration data was used to build a feeding detector. In blind trials on the remaining data, this detector accurately parsed true feeding from other motions (91-100% true positive rate, 0-4.8% false positive rate), improving upon similar published methods. In a second study, I used depth and acceleration data to estimate the changing body density of 8 wild sea lions throughout dives, and used those data to calculate each sea lion’s energetic expenditure during descent and ascent at fine temporal scales. Energy expenditure patterns closely followed the influence of buoyancy changes with depth. Importantly, sea lions used more energy per second but less energy per meter as dive depth increased, revealing high costs of deep diving. Combined, these studies further our understanding of California sea lion foraging ecology and provide new methods to aid similar future studies

Objectively measured physical activity and sedentary behaviour in young children

Study 1: Aims: Compare the uniaxial MTI/CSA accelerometer and the biaxial Actiwatch accelerometer against direct observation of total physical activity and minute-by-minute physical activity in 3-4 year olds. Methods: MTI/CSA-7164 and Actiwatch accelerometers simultaneously measured activity during 35-45 minute sessions of structured play in 78, 3-4 year olds. Rank order correlations between accelerometry and direct observation were used to assess the ability of the accelerometers to assess total activity. Within-child minute-by-minute correlations were calculated between accelerometry output and direct observation. Results: For assessment of total activity MTI/CSA output was significantly positively correlated with direct observation (r 0.72, p0.05). Conclusion: The present study suggests that for epidemiological assessment of total physical activity in young children the MTI/CSA-7164 provides greater accuracy than the Actiwatch. Study 2: Aim: To cross-validate the 1100 counts/ minute cut-off for the assessment of sedentary behaviour in an independent sample of young children using the MTI/CSA accelerometer. Methods: A previously developed cut-off for MTI-CSA accelerometry output (validation study) in 30 healthy Scottish 3-4 year olds, was cross-validated against direct observation in an independent sample of (n = 52) healthy Scottish 3-4 year olds. Results: In the cross-validation study sensitivity was 83%: 438/528 inactive minutes were correctly classified. Specificity was 82%: 1251/1526 non-inactive minutes were correctly classified using this cut-off. Conclusion: Sedentary behaviour can be quantified objectively in young children using accelerometry

Head-mounted accelerometry accurately detects prey capture in California sea lions

Detecting when and where animals feed is key to understanding their ecophysiology, but our ability to collect these data in marine mammals remains limited. Here, we test a tag-based accelerometry method to detect prey capture in California sea lions. From synchronized underwater video and acceleration data of two trained sea lions, we isolated a combined acceleration and Jerk pattern that reliably indicated prey capture in training datasets. We observed a stereotyped feeding motion in underwater video that included (1) mouth opening while approaching prey; (2) head deceleration to allow initial suction or prey engulfment, and (3) jaw closure. This motion (1–3) was repeated if a prey item was not initially engulfed. This stereotyped feeding motion informed a signal pattern phrase that accurately detected feeding in a training dataset. This phrase required (1) an initial heave-axis Jerk signal surpassing a threshold based on sampling rate; (2) an estimated dynamic surge-axis deceleration signal surpassing −0.7 g beginning within 0.2 s of the initial Jerk signal; and (3) an estimated dynamic surge-axis acceleration signal surpassing 1.0 g within 0.5 s of the beginning of the prior deceleration signal. We built an automated detector in MATLAB to identify and quantify these patterns. Blind tests of this detector on non-training datasets found high true-positive detection rates (91%–100%) with acceleration sampled at 50–333 Hz and low false-positive detection rates (0%–4.8%) at all sampling rates (16–333 Hz). At 32 Hz and below, true-positive detection rates decreased due to attenuation of signal detail. A detector optimized for an adult female was also accurate at 32–100 Hz when tested on an adult male’s data, suggesting the potential future use of a generalized detector in wild subjects. When tested on the same data, a published triaxial Jerk method produced high true-positive detection rates (91–100%) and low-to-moderate false-positive detection rates (15–43%) at ≥ 32 Hz. Using our detector, larger prey elicited longer prey capture duration in both animals at almost all sampling rates 32 Hz or faster. We conclude that this method can accurately detect feeding and estimate relative prey length in California sea lions

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Acknowledgments This project and the tags deployed on both seabird's species were fund by NERC (grant number NE/K007440/1), Marine Scotland Science and Seabird Tracking and Research (STAR) Project led by the Royal Society for the Protection of Birds (RSPB). We would like to thank Rob Hughes, Tessa Cole and Ruth Brown for helping in the data collection, the Bird Observatory of Fair Isle for supporting the fieldwork and the Marine Collaboration Research Forum (MarCRF).Peer reviewedPublisher PD