In natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align one\'s direction of travel, or heading, with one\'s goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task.

The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects.

Homing is often a critical aspect of an animal\'s behavioural and spatial ecology. Translocation is considered to be a wildlife management strategy that could reduce human-wildlife confrontation, but this strategy may not be effective if animals attempt to home to their original capture location. Translocation of animals from sites where possible human-wildlife interaction occurs is a widespread but controversial intervention to resolve conflicts. In India, snakes are often the subject of such translocations, but there is a paucity of information on the behaviour of translocated snakes compared to resident snakes. The Indian python (Python molurus), one of the largest carnivores in the Indian subcontinent, is classified as Near Threatened by the IUCN. We conducted a two-year radio-tracking study (December 2018 to December 2020) on the movements of 14 adult Indian pythons in the Moyar River Valley, within the Sathyamangalam and Mudumalai Tiger Reserves. Eleven of the 14 pythons were translocated 0.28-55.7 kms from their capture locations, while 3 pythons were not translocated: 6 were translocated short distances (<5 km from capture; range 0.28-4.67 kms), 2 were translocated to medium distances (9-11 kms from capture location), and 3 were translocated to long distances (21-55.7 kms from capture location). Four of the six snakes translocated short distances all returned to within 500 m of their original capture locations, and all 6 returned over 60 % of the translocated distance to the initial capture location. Of the two snakes translocated medium distances, both returned to within 1.1 km of the capture location (∼90 % of the distance home). None of the three snakes that were translocated long distances successfully returned to their capture locations. Translocated pythons exhibited greater net movement distances than resident snakes within the first 2 months of release. Based on these results, long-distance translocation may be an effective strategy to minimize human-python conflict, while short or medium distance translocation is unlikely to be successful. However, more research is needed about the long-term survival of translocated snakes as well as soft-release methodologies that could prevent aberrant movement behaviour directly following release.

Wayfinding systems using inertial data recorded from a smartphone carried by the walker have great potential for increasing mobility independence of blind pedestrians. Pedestrian dead-reckoning (PDR) algorithms for localization require estimation of the step length of the walker. Prior work has shown that step length can be reliably predicted by processing the inertial data recorded by the smartphone with a simple machine learning algorithm. However, this prior work only considered sighted walkers, whose gait may be different from that of blind walkers using a long cane or a dog guide. In this work, we show that a step length estimation network trained on data from sighted walkers performs poorly when tested on blind walkers, and that retraining with data from blind walkers can dramatically increase the accuracy of step length prediction.

The question of What is learned when navigating to a place is reinforced has been the subject of considerable debate. Prevailing views emphasize cognitive structures (e.g., maps) or associative learning, which has shaped measurement in spatial navigation tasks (e.g., the Morris water task [MWT]) toward selection of coarse measures that do not capture precise behaviors of individual animals. We analyzed the navigation paths of 15 rats (60 trials each) in the MWT at high temporal resolution (30Hz) and utilized dynamic time warping to quantify the similarity of paths within and between animals. Paths were largely direct, yet suboptimal, and included changes in speed and trajectory that were established early in training and unique to each animal. Individual rats executed similar paths from the same release point from trial to trial, which were distinct from paths executed by other rats as well as paths performed by the same rat from other release points. These observations suggest that rats learn to execute similar path sequences from trial to trial for each release point in the MWT. Occasional spontaneous deviations from the established, unique behavioral sequence, resulted in profound disruption in navigation accuracy. We discuss the potential implications of sequence navigation behaviors for understanding relations between behavior and spatial neural signals such as place cells, grid cells, and head direction cells.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40614-024-00402-8.

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.

UNASSIGNED: Many techniques and technologies exist for the intraoperative assessment of component positioning, leg lengths, and offset in total hip arthroplasty, but with limited comparative data. We conducted a systematic review of the available literature to evaluate the range of techniques and technologies for the intraoperative assessment of component position as well as leg lengths and offset in terms of accuracy, precision, surgical time, cost, and relationship to clinical outcomes.
UNASSIGNED: A comprehensive search of the Embase and Medline databases from 1974 to 2023 was performed. We included controlled or comparative prospective clinical studies. Cochrane\'s risk-of-bias tool for randomized trials and Risk of Bias in Non-Randomized Studies - of Interventions tools were used by 2 independent reviewers to evaluate each study for risk of bias. We conducted both qualitative and quantitative analyses of the studies included. However, a meta-analysis was deemed not to be feasible due to heterogeneity.
UNASSIGNED: Our review included 25 studies with 52 intraoperative techniques and technologies. Mechanical guides and computerized navigation were most frequently evaluated in the included studies. Computerized navigation systems consistently showed the greatest accuracy and precision across all measures, at the cost of longer mean operative time. In contrast, freehand techniques demonstrated the poorest accuracy and precision. Insufficient data were found to reach any meaningful conclusions in terms of differences in overall surgical cost or clinical outcomes.
UNASSIGNED: Evidence shows that computerized navigation systems are most accurate and precise in positioning components during total hip arthroplasty. Further research is needed to determine their health and economic impact and whether the accuracy and precision of navigated techniques are justified in terms of clinical outcomes.

UNASSIGNED: Total knee arthroplasty (TKA) is a commonly performed procedure that has traditionally utilized reproducible steps using a set of mechanical instruments. The number of TKAs performed using robotic assistance is increasing, and augmented reality (AR) navigation systems are being developed. Hierarchical task analysis (HTA) aims to describe the steps of a specific task in detail to reduce errors and ensure reproducibility. The objective of this study was to develop and validate HTAs for conventional, robotic-assisted, and AR-navigated TKA.
UNASSIGNED: The development of HTAs for conventional TKA involved an iterative review process that incorporated the input of 4 experienced arthroplasty surgeons. The HTAs were then adapted for robotic-assisted and AR-navigated TKA by incorporating specific steps associated with the use of these systems. The accuracy and completeness of the HTAs were validated by observing 10 conventional and 10 robotic-assisted TKA procedures.
UNASSIGNED: HTAs for conventional, robotic-assisted, and AR-navigated TKA were developed and validated. The resulting HTAs provide a comprehensive and standardized plan for each procedure and can aid in the identification of potential areas of inefficiency and risk. Robotic-assisted and AR-navigated approaches require additional steps, and there are an increased number of instances where complications may occur.
UNASSIGNED: The HTAs developed in this study can provide valuable insights into the potential pitfalls of robotic-assisted and AR-navigated TKA procedures. As AR-navigation systems are developed, they should be optimized by critical analysis using the developed HTAs to ensure maximum efficiency, reliability, accessibility, reduction of human error, and costs.

The rapid increase in telehealth has the potential to bring informed decision-making for prostate cancer screening (PCS) at the population level to high-risk individuals. We utilized a global technology platform of electronic health records data repositories (TriNetX) to determine its utility for Navigator-guided decision-making aid for PCS in Black men ages 45-79 years with no history of prostate cancer and PSA testing. Patients from Pennsylvania were invited to participate in a telehealth-delivered informed decision-making session for PCS. Focus groups, social learning theory, visual diagrams, and quantitative data on PCS risks and benefits were used to develop the content of the sessions, which included numerical discussions of risks vs. benefits in Black men. Participants completed several surveys, including baseline demographic and numeracy questionnaires, a one-on-one telehealth session with a trained Navigator, post-Navigation surveys, and an optional follow-up session with a urologist. Eighty-seven participants were consented and recruited. Although the mean numeracy score was only 1.9 out of 6, more than 90% rated as good or excellent that the sessions aided their PCS decision-making skills. This study indicates that Navigation by telehealth offers the ability to assist in informed decision-making for PCS at the population level.

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.