Exploring occupational standing activities using accelerometer-based activity monitoring

Prolonged standing at work is required by an estimated 60% of the employed population and is associated with a high prevalence of musculoskeletal disorders. ‘Standing’ is expected to encompass a range of activities of varying intensity. This study aimed to define a range of ‘standing’ work-based activities; and objectively explore differences between ‘standing’ occupations. The following movements were defined using a triaxial accelerometer (ActivPAL) through recordings of known movements (n = 11): static standing, weight-shifting, shuffling, walking and sitting. Movements over a working day were defined for chefs (n = 10), veterinary surgeons (n = 7) and office workers (n = 9). Despite veterinary surgeons and chefs spending a similar time in an upright posture, veterinary surgeons spent 62% of this time standing statically whereas chefs split their time between all the movements. Overall, this study provides the first attempt to define ‘standing’ activities, allowing the differentiation of activities between occupations spending similar periods of time upright. Practitioner Summary: This study identified a range of work-based ‘standing’ activities of varying intensity. Differences in activity were recorded between two occupations spending a similar time in an upright posture (veterinary surgeons and chefs). A broader definition of standing activities could be important when considering factors related to musculoskeletal disorders at work

Methods for the real-world evaluation of fall detection technology : a scoping review

Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-three articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in it’s infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures which depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems

A technique to record the sedentary to walk movement during free living mobility : a comparison of healthy and stroke populations

Background Hesitation between moving from a sedentary posture (lying/sitting) to walking is a characteristic of mobility impaired individuals, as identified from laboratory studies. Knowing the extent to which this hesitation occurs during everyday life would benefit rehabilitation research. This study aimed to quantify this transition hesitation through a novel approach to analysing data from a physical activity monitor based on a tri-axial accelerometer and compare results from two populations; stroke patients and age-matched unimpaired controls. Methods Stroke patients living at home with early supported discharge (n=34, 68.9YO ± 11.8) and age matched controls (n=30, 66.8YO ± 10.5) wore a physical activity monitor for 48hrs. The outputs from the monitor were then used to determine the transitions from sedentary to walking. The time delay between a sedentary posture ending and the start of walking classified four transition types: 1) fluent (<=2s), 2) hesitant (>2s<=10s), 3) separated (>10s) and 4) a change from sedentary with no registered walking to a return to sedentary. Results Control participants initiated walking after a sedentary posture on 92% of occasions. Most commonly (43%) this was a fluent transition. In contrast stroke patients walked after changing from a sedentary posture on 68% of occasions with only 9% of transitions classed as fluent, (p<0.05). Discussion/Conclusion A new data analysis technique reports the frequency of walking following a change in sedentary position in stroke patients and healthy controls and characterises this transition according to the time delay before walking. This technique creates opportunities to explore everyday mobility in greater depth

Are older people putting themselves at risk when using their walking frames?

Background Walking aids are issued to older adults to prevent falls, however, paradoxically their use has been identified as a risk factor for falling. To prevent falls, walking aids must be used in a stable manner, but it remains unknown to what extent associated clinical guidance is adhered to at home, and whether following guidance facilitates a stable walking pattern. It was the aim of this study to investigate adherence to guidance on walking frame use, and to quantify user stability whilst using walking frames. Additionally, we explored the views of users and healthcare professionals on walking aid use, and regarding the instrumented walking frames (‘Smart Walkers’) utilized in this study. Methods This observational study used Smart Walkers and pressure-sensing insoles to investigate usage patterns of 17 older people in their home environment; corresponding video captured contextual information. Additionally, stability when following, or not, clinical guidance was quantified for a subset of users during walking in an Activities of Daily Living Flat and in a gait laboratory. Two focus groups (users, healthcare professionals) shared their experiences with walking aids and provided feedback on the Smart Walkers. Results Incorrect use was observed for 16% of single support periods and for 29% of dual support periods, and was associated with environmental constraints and a specific frame design feature. Incorrect use was associated with reduced stability. Participants and healthcare professionals perceived the Smart Walker technology positively. Conclusions Clinical guidance cannot easily be adhered to and self-selected strategies reduce stability, hence are placing the user at risk. Current guidance needs to be improved to address environmental constraints whilst facilitating stable walking. The research is highly relevant considering the rising number of walking aid users, their increased falls-risk, and the costs of falls. Trial Registration Not applicable

A machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees

There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users

Does free-living physical activity improve one-year following total knee arthroplasty in patients with osteoarthritis : a prospective study

Objective Total knee arthroplasty (TKA) is the gold-standard treatment for end-stage knee osteoarthritis, and the primary expectations are reduced pain and improved function. However, there is conflicting evidence regarding functional changes post-TKA. Commonly, functional changes are measured using Oxford Knee Score (OKS). No previous study has investigated physical behaviour (PB) changes in terms of volume and patterns post-TKA. The aims of this study were to explore volume and pattern changes in PB following TKA using an objective tool and to assess the correlation between this and OKS. Design An activPAL measured the PB of individuals on a waiting list for TKA for a period of 7–8 days pre-TKA, and for the same length of time at 12 months post-TKA. OKS was completed at similar follow-up time points. Results Thirty-three individuals completed the study, where stepping time, the number of steps and the time spent on moderate to vigorous physical activity (MVPA) (>100 steps/minute) improved significantly post-TKA p = 0.0001. Steps at 12 months post-TKA improved by 45.6% (from 4,240 to 6,174) and stepping time increased by 38.8% (from 0.98 to 1.36 hours). MVPA improved by 35 minutes at 12 months (from 6.6 to 41.7 minutes). There were no significant correlations between PB and OKS. Conclusion This is the first study to explore PB volumes and event-based patterns post-TKA. Activity improved in terms of volume and patterns. No correlation was found between OKS and ActivPAL, which emphasises the need to use objective methods in addition to patient reported outcome measures

Quantification of outdoor mobility by use of accelerometer-measured physical behaviour

Hip fractures in older persons are associated with both low levels of daily physical activity and loss of outdoor mobility. The aim of this study was to investigate if accelerometer-based measures of physical behaviour can be used to determine if people undertake outdoor walking and to provide reference values for physical behaviour outcomes related to outdoor mobility in this population. Older persons (n=245) aged ≥70 years one year after hip fracture participated. Six objective outcome measures of physical behaviour collected by a thigh-mounted activity monitor were compared with self-reported outdoor mobility assessed with the Nottingham Extended ADL scale. All measures of time and length in upright (standing and walking) were significantly lower in participants who reported not to walk outdoors (p<0.001). A set of cut-off points for the different physical behaviour variables was generated. Maximum length of upright events discriminated best between groups, with 31 minutes as a threshold to determine if a person is more likely to report that they walk outdoors (sensitivity: 0.805, specificity: 0.704, AUC: 0.871), or 41 minutes or more to determine if a person is more likely to report outdoor walk on their own (sensitivity: 0.802, specificity: 0.833, AUC: 0.891). Physical behaviour variables from activity monitoring can provide information about patterns of physical behaviour related to outdoor activity performance