The purpose of this study was to investigate the relationship between movement velocity and relative load in three lower limbs exercises commonly used to develop strength: leg press, full squat and half squat. The percentage of one repetition maximum (%1RM) has typically been used as the main parameter to control resistance training; however, more recent research has proposed movement velocity as an alternative. Fifteen participants performed a load progression with a range of loads until they reached their 1RM. Maximum instantaneous velocity (V_max) and mean propulsive velocity (MPV) of the knee extension phase of each exercise were assessed. For all exercises, a strong relationship between V_max and the %1RM was found: leg press (r²_adj = 0.96; 95% CI for slope is [−0.0244, −0.0258], P < 0.0001), full squat (r²_adj = 0.94; 95% CI for slope is [−0.0144, −0.0139], P < 0.0001) and half squat (r²_adj = 0.97; 95% CI for slope is [−0.0135, −0.00143], P < 0.0001); for MPV, leg press (r²_adj = 0.96; 95% CI for slope is [−0.0169, −0.0175], P < 0.0001, full squat (r²_adj = 0.95; 95% CI for slope is [−0.0136, −0.0128], P < 0.0001) and half squat (r²_adj = 0.96; 95% CI for slope is [−0.0116, 0.0124], P < 0.0001). The 1RM was attained with a MPV and V_max of 0.21 ± 0.06 m s⁻¹ and 0.63 ± 0.15 m s⁻¹, 0.29 ± 0.05 m s⁻¹ and 0.89 ± 0.17 m s⁻¹, 0.33 ± 0.05 m s⁻¹ and 0.95 ± 0.13 m s⁻¹ for leg press, full squat and half squat, respectively. Results indicate that it is possible to determine an exercise-specific %1RM by measuring movement velocity for that exercise.

The prevalence of inter-limb strength differences is well documented in the literature however, there are inconsistencies related to measurement and reporting, and the normative values and effects associated with inter-limb asymmetry. Therefore, the aims of this systematic review were to: 1) assess the appropriateness of existing indices for the calculation of asymmetry, 2) interrogate the evidence basis for literature reported thresholds used to define asymmetry and 3) summarise normative levels of inter-limb strength asymmetry and their effects on injury and performance. To conduct this systematic review, scientific databases (PubMed, Scopus, SPORTDiscus and Web of Science) were searched and a total of 3,594 articles were retrieved and assessed for eligibility and article quality. The robustness of each identified asymmetry index was assessed, and the evidence-basis of the identified asymmetry thresholds was appraised retrospectively using the references provided. Fifty-three articles were included in this review. Only four of the twelve identified indices were unaffected by the limitations associated with selecting a reference limb. Eighteen articles applied a threshold to original research to identify “abnormal” asymmetry, fifteen of which utilised a threshold between 10-15%, yet this threshold was not always supported by appropriate evidence. Asymmetry scores ranged between and within populations from approximate symmetry to asymmetries larger than 15%. When reporting the effects of strength asymmetries, increased injury risk and detriments to performance were often associated with larger asymmetry, however the evidence was inconsistent. Limitations of asymmetry indices should be recognised, particularly those that require selection of a reference limb. Failure to reference the origin of the evidence for an asymmetry threshold reinforces doubt over the use of arbitrary thresholds, such as 10-15%. Therefore, an individual approach to defining asymmetry may be necessary to refine robust calculation methods and to establish appropriate thresholds across various samples and methodologies that enable appropriate conclusions to be drawn.

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable.

Background: Many older adults prefer to remain in their own homes for as long as possible. However, there are still questions surrounding how best to ensure that an individual can cope with autonomous living. Technological monitoring systems are an attractive solution; however, there is disagreement regarding activities of daily living (ADL) and the optimal technologies that should be used to monitor them. Objective: This study aimed to understand older adults’ perceptions of important ADL and the types of technologies they would be willing to use within their own homes. Methods: Semistructured interviews were conducted on the web with 32 UK adults, divided equally into a younger group (aged 55-69 years) and an older group (≥70 years). Results: Both groups agreed that ADL related to personal hygiene and feeding were the most important and highlighted the value of socializing. The older group considered several activities to be more important than their younger counterparts, including stair use and foot care. The older group had less existing knowledge of monitoring technology but was more willing to accept wearable sensors than the younger group. The younger group preferred sensors placed within the home but highlighted that they would not have them until they felt that daily life was becoming a struggle. Conclusions: Overall, technological monitoring systems were perceived as an acceptable method for monitoring ADL. However, developers and carers must be aware that individuals may express differences in their willingness to engage with certain types of technology depending on their age and circumstances.

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg^-1) and joint powers (W.kg^-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors’ knowledge, this is the first study to optimise the development of a machine learning algorithm.

The aim of this study was to generate a subject-specific musculoskeletal muscle model, based on isometric and isovelocity measurements of the whole lower extremity. A two-step optimization procedure is presented for optimizing the muscle-tendon parameters (MTPs) for isometric and isovelocity joint torque profiles. A significant improvement in the prediction of joint torque profiles for both the solely isometric and a combined isometric and dynamic method of optimization when compared to the standard scaling method of the AnyBody Modeling System (AMS) was observed. Depending on the specific purpose of the model, it may be worth considering whether the isometric-only would be sufficient, or the additional dynamic data are required for the combined approach.