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Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.

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<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m²) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

Soil erosion and contamination are two main desertification indices or land degradation agents in agricultural areas. Global climate change consequence is a priority to predict global environmental change impacts on these degradation risks. This agro-ecological approach can be especially useful when formulating soil specific agricultural practices based on the spatial variability of soils and related resources to reverse environmental degradation. Raizal and Pantanal models within the new MicroLEIS framework, the Ero&Con package, are database/expert system evaluation approach for assessing limitations to land use, or vulnerability of the land to specified agricultural degradation risks. This study was performed in Souma area with approximately 4100 ha extension in the North-West of Iran (west Azarbaijan). Based on 35 sampling soils, Typic Xerofluvents, Typic Calcixerepts, Fluventic Haploxerepts and Fluventic Endaquepts were classified as main subgroups. Climatological data, referred to temperature and precipitation of more than 36 consecutive years were collected from Urmieh station reports and stored in monthly Climate Database CDBm, as a major component of MicroLEIS DSS (CDBm) program. Climate data for a hypothetical future scenario were collected from the Intergovernmental Panel on Climate Change (IPCC) reports for the 2080s period. The evaluation approach predicts that attainable water erosion vulnerability classes were none (V1) very low (V2) and moderately low (V4) in the total of 72%, 13% and 15% of the Souma area, respectively and they will not affected by climate change. On contrary, attainable wind erosion vulnerability classes will increase. Also, phosphorous and heavy metal contamination vulnerability risks will not differ in two compared scenarios while nitrogen and pesticides vulnerability classes will be improved.

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To generate dynamic motions such as hopping and running on legged robots, model-based approaches are usually used to embed the well studied spring-loaded inverted pendulum (SLIP) model into the whole-body robot. In producing controlled SLIP-like behaviors, existing methods either suffer from online incompatibility or resort to classical interpolations based on lookup tables. Alternatively, this paper presents the application of a data-driven approach which obviates the need for solving the inverse of the running return map online. Specifically, a deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. The references are then mapped to the whole-body model through a QP-based inverse dynamics controller. Simulation experiments on the WALK-MAN robot are conducted to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions.