Electrode activity towards the negative and positive half-cell reactions of a vanadium flow battery were investigated. Cyclic voltammetry and electrochemical impedance spectroscopy were employed to monitor electrode activity of glassy carbon electrodes towards VII-VIII and VIV-VV redox reactions during electrochemical ageing through repeated anodic and cathodic treatments. Electrode activity is found to increase with number of treatment steps, showing little difference initially between anodised and cathodised electrodes. However, after several treatments, a potential-differentiated behaviour emerges, with distinct enhanced and inhibited states. For VII-VIII, anodised electrodes showed enhanced activity, while cathodised electrodes were inhibited. Conversely, for VIV-VV, cathodised electrodes had enhanced activity. In almost all cases, the activity is greater than that of an untreated electrode. Eventually, electrode activities stabilise in a steady-state region where activity depends on the final treatment potential rather than the number of steps. In this region, activity can be toggled reproducibly between enhanced and inhibited states. Therefore, it can be concluded that functional groups, rather than surface roughening or defect formation, are responsible for this toggling capability. Furthermore, for VIV-VV, steady-state activity levels and the number of treatment steps required to reach this region are found to be dependent on the upper and lower treatment potentials.

Purpose: Knee osteoarthritis (OA) is currently evaluated clinically using structural imaging. Meaningful change in biomechanical gait variables has been strongly associated with disease progression in knee OA, which can help to advance the understanding of this disease progression. Measurement of gait biomechanics is often performed in laboratories using optical motion capture systems and floor mounted force plates. However, these systems are expensive, not conveniently portable and require skilled staff to operate.

Advances in sensor technology have provided an opportunity to measure gait characteristics using body-worn inertial measurement units (IMUs). Whilst research investigating the validity of IMUs in reporting gait characteristics is extensive, research investigating the reliability of IMUs is limited. This study aimed to investigate the inter-session reliability of wireless IMU derived measures of gait (i.e., knee angle, range of motion) taking multiple test administrators into account. Fifteen healthy volunteers (43 ± 15 years) completed two visits. Within each visit, participants were required to perform two sets of 6 gait trials (6-metre walk tests). IMUs were placed on the participant in 7 locations on the lower limbs and waist. A different test administrator (n = 3) applied the IMUs at each set. At visit 2, this procedure was repeated with the same test administrators as visit 1. Kinematic measures of maximum angle (Knee_Max), minimum angle (Knee_Min), and range of motion (RoM) are reported for the left and right knee. The intraclass correlation coefficients (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) are reported to determine IMU reliability. The results confirmed moderate to good inter-session reliability across all features (0.73-0.87). SEM values ranged from 1.21-3.32° and MDC values ranged from 3.37 – 9.21°. Therefore, IMUs appear to be a reliable method to determine inter-session gait characteristics across multiple test administrators.

When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features. A proof-of-concept of the algorithm, which relies on cross-correlation of gyroscope sensor data from the shank and foot, to align the sensor systems is demonstrated. The algorithm output is validated against those signals synchronized using manually annotated heel-strike and toe-off ground-truth signal landmarks, identified in both the shank and feet signals using previously published definitions. Results demonstrate that the developed algorithm is capable of synchronizing both sensor systems, based on IMU data from both healthy participants and participants suffering from knee osteoarthritis, with a mean lag time bias of 25.56ms when compared to the ground truth. A proof-of-concept of technique to synchronise IMUs attached to the shanks and insoles attached at the feet is demonstrated and offers an alternative approach to sensor system synchronisation.

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.

Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility of assessing PwMS and Healthy Control's (HC) physical function by characterising gaitrelated features, which can be modelled using machine learning (ML) techniques to correctly distinguish subgroups of PwMS from healthy controls. A total of 97 subjects (24 HC subjects, 52 mildly disabled (PwMSmild, EDSS [0-3]) and 21 moderately disabled (PwMSmod, EDSS [3.5- 5.5]) contributed data which was recorded from a TwoMinute Walk Test (2MWT) performed out-of-clinic and daily over a 24-week period. Signal-based features relating to movement were extracted from sensors in smartphone and smartwatch devices. A large number of features (n = 156) showed fair-to-strong (R > 0.3) correlations with clinical outcomes. LASSO feature selection was applied to select and rank subsets of features used for dichotomous classification between subject groups, which were compared using Logistic Regression (LR), Support Vector Machines (SVM) and Random Forest (RF) models. Classifications of subject types were compared using data obtained from smartphone, smartwatch and the fusion of features from both devices. Models built on smartphone features alone achieved the highest classification performance, indicating that accurate and remote measurement of the ambulatory characteristics of HC and PwMS can be achieved with only one device. It was observed however that smartphonebased performance was affected by inconsistent placement location (running belt versus pocket). Results show that PwMSmod could be distinguished from HC subjects (Acc. 82.2 ± 2.9%, Sen. 80.1 ± 3.9%, Spec. 87.2 ± 4.2%, F 1 84.3 ± 3.8), and PwMSmild (Acc. 82.3 ± 1.9%, Sen. 71.6 ± 4.2%, Spec. 87.0 ± 3.2%, F1 75.1 ± 2.2) using an SVM classifier with a Radial Basis Function (RBF). PwMSmild were shown to exhibit HC-like behaviour and were thus less distinguishable from HC (Acc. 66.4 ± 4.5%, Sen. 67.5 ± 5.7%, Spec. 60.3 ± 6.7%, F 1 58.6 ± 5.8). Finally, it was observed that subjects in this study demonstrated low intraand high inter-subject variability which was representative of subject-specific gait characteristics.

BACKGROUND:People living with multiple sclerosis (MS) experience impairments in gait and mobility, that are not fully captured with manually timed walking tests or rating scales administered during periodic clinical visits. We have developed a smartphone-based assessment of ambulation performance, the 5 U-Turn Test (5UTT), a quantitative self-administered test of U-turn ability while walking, for people with MS (PwMS). RESEARCH QUESTION:What is the test-retest reliability and concurrent validity of U-turn speed, an unsupervised self-assessment of gait and balance impairment, measured using a body-worn smartphone during the 5UTT? METHODS:76 PwMS and 25 healthy controls (HCs) participated in a cross-sectional non-randomised interventional feasibility study. The 5UTT was self-administered daily and the median U-turn speed, measured during a 14-day session, was compared against existing validated in-clinic measures of MS-related disability. RESULTS:U-turn speed, measured during a 14-day session from the 5UTT, demonstrated good-to-excellent test-retest reliability in PwMS alone and combined with HCs (intraclass correlation coefficient [ICC] = 0.87 [95 % CI: 0.80-0.92]) and moderate-to-excellent reliability in HCs alone (ICC = 0.88 [95 % CI: 0.69-0.96]). U-turn speed was significantly correlated with in-clinic measures of walking speed, physical fatigue, ambulation impairment, overall MS-related disability and patients' self-perception of quality of life, at baseline, Week 12 and Week 24. The minimal detectable change of the U-turn speed from the 5UTT was low (19.42 %) in PwMS and indicates a good precision of this measurement tool when compared with conventional in-clinic measures of walking performance. SIGNIFICANCE:The frequent self-assessment of turn speed, as an outcome measure from a smartphone-based U-turn test, may represent an ecologically valid digital solution to remotely and reliably monitor gait and balance impairment in a home environment during MS clinical trials and practice.

The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population.

OBJECTIVES:It remains unclear if geriatric patients with different delirium motor subtypes express different levels of motor activity. Thus, we used two accelerometer-based devices to simultaneously measure upright activity and wrist activity across delirium motor subtypes in geriatric patients.DESIGN:Cross-sectional study.SETTINGS:Geriatric ward in a university hospital in Norway.PARTICIPANTS:Sixty acutely admitted patients, ≥75 years, with DSM-5-delirium.OUTCOME MEASURES:Upright activity measured as upright time (minutes) and sit-to-stand transitions (numbers), total wrist activity (counts) and wrist activity in a sedentary position (WAS, per cent of the sedentary time) during 24 hours ongoing Delirium Motor Subtype Scalesubtyped delirium.RESULTS:Mean age was 86.7 years. 15 had hyperactive, 20 hypoactive, 17 mixed and 8 had no-subtype delirium. We found more upright time in the no-subtype group than in the hypoactive group (119.3 vs 37.8 min, p=0.042), but no differences between the hyperactive, the hypoactive and the mixed groups (79.1 vs 37.8 vs 50.1 min, all p>0.28). The no-subtype group had a higher number of transitions than the hypoactive (54.3 vs 17.4, p=0.005) and the mixed groups (54.3 vs 17.5, p=0.013). The hyperactive group had more total wrist activity than the hypoactive group (1.238×104 vs 586×104 counts, p=0.009). The hyperactive and the mixed groups had more WAS than the hypoactive group (20% vs 11%, p=0.032 and 19% vs 11%, p=0.049).CONCLUSIONS:Geriatric patients with delirium demonstrated a low level of upright activity, with no differences between the hyperactive, hypoactive and mixed groups, possibly due to poor gait function. The hyperactive and mixed groups had more WAS than the hypoactive group, indicating true differences in motor activity across delirium motor subtypes, also in geriatric patients. Wrist activity appears more suitable than an upright activity for both diagnostic purposes and activity monitoring in geriatric delirium.

Background: This study aims to perform a concurrent criterion validation of the activPAL3 activity monitor, in the detection of physical activity, steps, and postural transfers in older adults using video observation. Method: Twenty community-dwelling older adults performed both an unsupervised free-living activity protocol in their home environment, recorded using body-worn cameras, and a semi-structured supervised protocol in a smart-home setting, recorded using wall mounted cameras, with an activPAL3 attached to the thigh. Percentage of agreement and typical statistical accuracy metrics were calculated by comparing the activPAL3 output and the video observation gold-standard (0.04 s resolution). Results: The activPAL3 provided a valid measure of standing, sitting, lying, and purposeful walking, including stair climbing. Shuffling, picking, transition, and kneeling were not consistently classified when compared to video observation and were thus confounding activities for the activPAL3. Sedentary behavior was better identified in a free-living scenario than during the semi-structured protocol. Step detection during stair ascending and descending achieved a high percentage of agreement (>89%). Steps detected during walking were underreported (80.2% free-living, 72.9% laboratory-based). Many steps were not detected during shuffling and transitions; overall, the percentage of agreement was low (59.5% free-living, 58% laboratory-based). Good sensitivity, specificity, and accuracy (>85%) were achieved for laboratory-based activities and good to excellent sensitivity, specificity, and accuracy (>89%) were achieved for free-living activities. Percentage of agreement was higher for free-living activities (85.2%) compared to laboratory-based activities (69.15%). Conclusion: This validation study provided a detailed insight into the physical activities that the activPAL3 classifies in its three main activity categories, step detection and postural transition analysis in a laboratory and a free-living setting. Caution is advised when measuring relatively more intensive physical activity protocols (e.g., in-lab), assessing postural transfer quantity, or during sedentary behavior analysis, as some short-duration sedentary bouts are ignored and postural transfers underreported.

Objectives: The development of a reliable method for the identification of sedentary, light and moderate physical activities in older adults. The method consists of a validated set of definitions for the identification of the initiation and termination of physical activities performed by older adult participants, video recorded during free-living and a laboratory setting. Design: Inter-rater reliability assessment in a fully crossed design. Methods: An iterative consensus process was used to define the initiation and termination of common activities of daily living. These definitions were then tested using videos recorded in two scenarios (1) by 9 raters who annotated a video recording, of a free-living protocol in a home environment, recorded in a first person view, using a body-worn camera and (2) by 7 raters who annotated a video recording, of older adults performing a semi-structured protocol in a living-lab environment, recorded in a third person view, using wall mounted cameras. Results: Inter-rater reliability was excellent for all items, with Krippendorff's alpha and Fleiss' kappa all above 0.84 and a percentage of agreement above 88%. All ICC(C,1) inter-rater values for the activity quantity and duration were all above 0.9. Conclusions: This set of physical activity initiation and termination definitions offers independent researchers a gold standard method to allow for the consistent annotation of high-frequency video footage (25fps), in both a free-living and laboratory setting. When synchronised with body-worn or ambient sensors, this annotation will allow for the development and validation of physical activity classification systems to a higher resolution than before. (C) 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement.We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors.Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities.Video labelling of the subjects' movements was performed by five raters using 11 different category labels.The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86).A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities.A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively.This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently.This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.

Aggressive and challenging behaviors in elderly patients are difficult to manage in the hospital. The benefit of early geriatric intervention in severely agitated patients in the hospital is not known. The aim of this study was to determine the outcome of early geriatric intervention for severely agitated geriatric patients requiring code blacks (standard emergency code for violent patients/ behavioral situation) at a tertiary hospital. A retrospective study of severely agitated geriatric patients was conducted using existing medical records and electronic database comparing a group who received early geriatric intervention with a group of patients who received standard of care. Forty-six patients were analyzed, 24 patients received early geriatric intervention and 22 patients received standard care. There was reduced number (1.42 versus 2.54, p = 0.002) and types of antipsychotic medications used in the early geriatric intervention group compared with the control group. The use of behavior chart was higher in the early geriatric intervention group (100% versus 41%, p < 0.01). There were more patients who required 5 or more code blacks in the control group. There was no significant difference in length of hospital stay, total number of code blacks, institutionalization, mortality, use of restraints, and safety incidents. Early geriatric intervention in severely agitated patients showed better outcome in reducing the use of antipsychotic medications and ensuring their appropriate use. Non-pharmacologic strategies were emphasized as the mainstay of treatment in managing patient’s behavioral disturbance.

Using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), it was shown for four different types of carbon that electrode treatments at negative potentials enhance the kinetics of V-IV-V-V and inhibit the kinetics of V-II-V-III while electrode treatments at positive potentials inhibit the kinetics of V-IV-V-V and enhance the kinetics of V-II-V-III. These observations may explain conflicting reports in the literature. The potentials required for activation and deactivation of electrodes were examined in detail. The results suggest that interchanging the positive and negative electrodes in a vanadium flow battery (VFB) would reduce the overpotential at the negative electrode and so improve the performance. This is supported by flow-cell experiments. Thus, periodic catholyte-anolyte interchange, or equivalent alternatives such as battery overdischarge, show promise of improving the voltage efficiency of VFBs.

This pilot study assessed the feasibility of using first person (1P) video recording with Google Glass (GG) to assess procedural skills, as compared with traditional third person (3P) video. We hypothesized that raters reviewing 1P videos would visualize more procedural steps with greater inter-rater reliability than 3P rating vantages.Seven subjects performed simulated internal jugular catheter insertions. Procedures were recorded by both Google Glass and an observer's head-mounted camera. Videos were assessed by 3 expert raters using a task-specific checklist (CL) and both an additive- and summative-global rating scale (GRS). Mean scores were compared by t-tests. Inter-rater reliabilities were calculated using intraclass correlation coefficients.The 1P vantage was associated with a significantly higher mean CL score than the 3P vantage (7.9 vs 6.9, P = .02). Mean GRS scores were not significantly different. Mean inter-rater reliabilities for the CL, additive-GRS, and summative-GRS were similar between vantages.1P vantage recordings may improve visualization of tasks for behaviorally anchored instruments (eg, CLs), whereas maintaining similar global ratings and inter-rater reliability when compared with conventional 3P vantage recordings.