By Dr Adamczyk.

How can real-world data be used to gain detailed knowledge of how interventions such as prosthetic feet or rehabilitation programs affect real-world movement? The wide variability of a person’s activities is a confounding factor in simple long-term monitoring: people move very differently on a casual stroll than when walking for exercise; in the office compared to in a park; or when hurrying for a bus compared to gently stepping over ice. With all these and more included in the data, how can a scientist know that any differences observed are because of the intervention, and not just an artifact of circumstances? Our study addresses these challenges the same way a researcher would in a laboratory: by comparing only specific movements in repeated environments, focusing on a single controlled experimental difference – the intervention targeted for study.

Our paper presents methods to find and analyze these repeated conditions from long-duration data recorded in everyday life. We describe an improved technique to combine GPS recordings of outdoor location with data from foot-mounted wearable movement sensors, to reconstruct the location of movements throughout the day, including during indoor periods. We identify straight walking paths that are repeated frequently on multiple days, and discard other data to eliminate variability due to different circumstances. Finally, we analyze detailed movement of the foot to study characteristics like stride length, stride width and foot clearance at specific walking speeds on these specific comparable paths. To illustrate the method, one subject wore the sensors on his foot for one week with athletic shoes and another week with sandals. The subject logged over 48,000 foot movements, including 27,000 strides on straight paths and nearly 5,000 on frequently-repeated paths. Data from the frequent paths had substantially lower variability than the overall record of all straight walking. Detailed analysis showed subtle but persistent differences in stride length (longer with sandals) and foot clearance (greater with sandals).

The approach we developed – limiting analysis to conditions that are repeated frequently – is important to improve the quality and precision of comparisons based on real-world data. The key contributions are the idea and method to focus on repeatable movements within everyday life. We plan to study biomechanical movement differences with different prosthetic feet and orthoses; many other interventions could also be studied using the same approach. In future work, we plan to further improve the location reconstruction and to analyze other features such as walking during turning and negotiating stairs. We will also add features to study more detailed changes in movement and to reject additional sources of variability such as weather, crowds and carriage load.

Figure: The algorithm for path matching eliminates routes that are unique or poorly reconstructed, leaving only those that are repeated multiple times and ultimately those that are most frequently repeated. Top: all matched paths in the vicinity of a subject’s workplace are shown, with different colors for matched groupings (solid with shoes and dashed with sandals). Frequently repeated paths (at least 300 strides over 3 days, per condition) are circled. Bottom left: statistics using all straight-line walking captures many strides, but the high variability can obscure subtle differences between conditions (dark: shoes, light: sandals). Bottom right: Further focusing on frequently repeated paths (here path ii) reduces this variability, allowing better comparison of performance differences between conditions at matched movement speeds (vertical dashed lines). Trend lines in both plots show linear ANCOVA fits to each condition’s data.

Publication

Wang, W.; Adamczyk, P.G. Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths. Sensors 2019, 19, 1925. https://doi.org/10.3390/s19081925

About the Author

Fatigue is a common complaint for older people; more than 50 per cent of people aged 70+ report fatigue in undertaking their daily activities. Laboratory-induced fatigue has been shown to affect sensory and movement functions that are associated with falling, such as strength, balance and limb sensation. In addition, fatigue is likely to affect cognitive functions, such as processing speed and attention, which are essential to maintain balance. Therefore, a busy day of shopping, social activities or minding the grandchildren might leave an older person at risk of falling late in the day.

Previous studies showing fatigue effects have employed rigorous laboratory protocols that are unlikely to accurately reflect an older person’s daily activities. So, we asked a group of 50 healthy older people to plan a busy day (where they tried to fit in as many chores or physical activities as possible) and compared changes in fall-related measures of physical and cognitive function with a planned rest day (in which they tried to avoid activity by relaxing, reading or watching TV etc.). Using activity monitors, we found our participants undertook twice as many steps and 2.5 times more minutes of activity on the busy day, compared with the rest day. Participants reported an increase in feelings of fatigue following the busy day and no change in fatigue following the rest day (see figure). However, performance on physical and cognitive tests associated with fall risk changed similarly across the busy and rest days, suggesting that a busy day has little effect on factors associated with fall risk in older people.

Figure: Group mean (SD) self-reported fatigue and physiological fall risk score in the morning (am) and afternoon (pm) of the busy and rest days.

Our busy day protocol did not result in reduced strength, balance and stepping performance in older people, which is different from studies of immediate fatigue conducted with arduous fatiguing protocols in laboratory settings, but similar to studies of physical activity that increased reported tiredness. If any detrimental effects of increased activities undertaken during the busy day occurred, they were not long lasting and had dissipated by the time that the afternoon assessments were undertaken (approx. 4pm). Overall, the findings suggest that in the afternoon of a busy day (based on older people’s estimates of their busiest days), cognitive and physical functions associated with fall risk are minimally affected in healthy older people.

Publication

Sturnieks DL, Yak SL, Ratanapongleka M, Lord SR, Menant JC. A busy day has minimal effect on factors associated with falls in older people: An ecological randomised crossover trial. Exp Gerontol. 2018 12;106:192-197.

https://doi.org/10.1016/j.exger.2018.03.009

About the Author

Walking is one of the most fundamental human activities in daily life. Objective and accurate assessment of human gait provides valuable information to evaluate an individual’s gait pattern, diagnose gait abnormalities, and devise rehabilitation to restore or optimise the gait pattern. Currently, the majority of gait event detection methods are based on kinematic characteristics of trunk or foot movements, which can be obtained using inertial sensors, insoles with built-in pressure sensors or a combination of the above. All these devices have their own disadvantages. The inertial-based devices are accurate for detecting gait events during moderate and fast walking. However, their performance noticeably degrades at lower walking speeds, which is usually the pace for individuals with difficulty in walking. Foot switches and insole pressure sensors are not able to detect gait events during the swing phase, when the foot is not in contact with the floor.

Force myography (FMG) can be a promising solution for gait event detection. FMG is a muscle activity sensing technology that often utilizes an array of force pressure sensors surrounding a limb to register volumetric changes of the underlying musculotendinous complex during activity. FMG has recently gain interest, and has been investigated primarily for upper extremity gesture recognition and prosthesis control. The MENRVA lab (http://www.sfu.ca/menrva.html) has conducted a series of studies to explore the feasibility of a FMG ankle band for gait analysis, including detecting ankle movements, counting steps, and gait event detection. In this study, we used a FMG ankle band with an array of eight force sensing resistors (FSRs) on the ankle (Fig.1) to record the pressure changes during walking. Healthy young volunteers were recruited to walk slowly on a treadmill. A machine learning model was built on a part of the collected FMG data to detect gait events and partition gait phases. The preliminary results show that over 98.5% steps were correctly detected.

The current FMG ankle band is designed to accurately monitoring slow gait in senior people. We plan to further improve the performance of the strap and test the accuracy of this novel method to a wider population including stroke survivors, Parkinson’s disease, and multiple sclerosis. This technology is anticipated to be transferred to clinical settings and has the potential to improve the rehabilitation process and quality of life of those affected by gait impairments caused by various neurological conditions.

Figure: 1) The FSR (force sensing resistors) band for signal acquisition; 2) the strap placement on the ankle; and 3) the FMG (force myography) signals corresponding to detected gait phases. IC: Initial-Contact, MSt: Mid-Stance, PS: Pre-Swing, Sw: Swing.

Publication
K.H. Chu, X. Jiang, C. Menon, Wearable step counting using a force myography-based ankle strap, J. Rehabil. Assist. Technol. Eng. 4 (2017) 1–11. doi:10.1177/2055668317746307. URL

About the Author

Balance, strength and physical activity are important factors for healthy ageing and preventing age-related functional decline. In order to be effective, preventive interventions must target risk factors for functional decline, be tailored to the needs and preferences of the individual, and be designed to change behaviour to a sustained healthier lifestyle. Smartphones and smartwatches are used by an increasing number of people, with thousands of smartphone applications available to promote healthy lifestyles. However, few of these applications are evidence based, meaning that their contribution to overcoming the challenges presented by an ageing population is limited.

The European Project ‘PreventIT’ (EU Horizon 2020 Grant Agreement No. 689238) aims to address this issue, by developing an evidence-based mHealth behaviour change intervention. PreventIT has adapted the Lifestyle-integrated Functional Exercise (LiFE) programme, which reduced falls in people 75 years and over (BMJ 2012; 345:e4547), for a younger cohort (aLiFE). The aLiFE programme incorporates challenging strength and balance/agility tasks, as well as specific recommendations for increasing physical activity in young-older adults, aged 60-70 years. Personalised advice is given on how to integrate strength, balance and physical activities into existing daily routines. aLiFE was then operationalised to be delivered using smartphones and smartwatches (eLiFE), providing the opportunity to send timely motivational messages and real-time feedback to the user. Both aLiFE and eLiFE are behaviour change interventions, supporting older adults to form long term physical activity habits. PreventIT has taken the original LiFE concept and further developed the behaviour change elements, explicitly relating and mapping them to Social Cognitive Theory and behaviour change techniques. Goal setting, planning, prompts and real-time feedback are used to deliver a person-centred experience for participants in the intervention. Findings from the aLiFE and eLiFE pilot studies highlighted the feasibility and acceptability of the PreventIT motivational strategy, with the vast majority of the participants rating the programmes positively (satisfaction score median: 6 points, out of maximum 7).

Mobile technology such as smartphones and smartwatches can be used effectively to monitor behaviour and to deliver a personalised intervention. The PreventIT mHealth intervention focusses on behaviour change from initiation to long-term maintenance, addressing the different phases of adopting a healthier lifestyle. As such, it makes a strong contribution to the developing field of evidence-based mHealth. The interventions (aLiFE and eLiFE) are currently being trialled in a three-site, three-arm feasibility randomised controlled trial in Norway, the Netherlands and Germany. An overview of the project can been viewed on YouTube: https://www.youtube.com/watch?v=upAfGHbNvdU

Publication:

Helbostad JL, Vereijken B, Becker C, Todd C, Taraldsen K, Pijnappels M, Aminian K, Mellone S. Mobile Health Applications to Promote Active and Healthy Ageing. Sensors. 2017; 17(3):622. http://www.mdpi.com/1424-8220/17/3/622

About the Author

Assessment of free-living gait is becoming increasingly popular in mobility and fall-risk research because of its ecological validity and advances in sensor technology. However, an essential step herein is detecting when someone is walking, which is not as easy as it seems. Previous studies have reported boundaries in separating gait from other activities as shuffling and standing still. Furthermore, inaccurate and instable placement of a sensor were described as difficulties for proper gait detection, even though these factors are hard to avoid when older adults wear sensors without supervision. Therefore, we set up a study to develop an algorithm for reliable gait detection from inertial sensors without the need for rigid sensor placement.

Fifty-one older adults with an average age of 83 years wore the Philips Senior Mobility Monitor, containing a triaxial accelerometer and a barometer, on a lanyard around their neck. We randomly placed the device underneath or outside clothing and changed the length of the lanyard between participants to mimic real-life variations. While being filmed, participants walked in a semi-controlled environment and performed free-living activities such as climbing stairs and sit-to-stand transfers. We annotated bouts of level-ground gait (excluding shuffling) in the videos as gold standard. To differentiate gait from other activities, we developed a wavelet-based decision tree algorithm.  Data of half of the participants was used to optimize algorithm thresholds (e.g. minimal number of heel strikes), while data of the other half of the participants was kept for validation. Subsequently, the algorithm was used to detect walking episodes in the sensor data of the validation group, which strongly corresponded to those annotated in the videos with high accuracy (≥97%) and low false-positive errors (≤1.9%) (Fig. 1).

To identify whether gait parameters from free-living gait were related to standardized laboratory-assessed gait parameters, we calculated the median and maximum cadence from the inertial sensor data and compared those with cadence obtained from three walks on a 10-m GaitRite walkway. We found that median and maximum cadence were strongly correlated with cadence measured on the GaitRite. Despite this strong correlation, median cadence was significantly lower in free-living gait compared to cadence measured on the GaitRite.

Our novel wavelet-based method is feasible for detecting free-living gaits from a pendant-worn inertial sensor. Giving the fact that the exact location of the sensor differed between participants and could even change during the experiment, this method is promising to detect gait in the home environment. As laboratory gait parameters where related to, but more equal to someone’s ‘best’ free-living gait parameters, home assessments have the potential to provide additional information for investigating daily performance.

Publication

Brodie MAD, Coppens MJM, Lord SR, Lovell NH, Gschwind YJ, Redmond SJ, Del Rosario M, Wang K, Sturnieks DL, Persiani M, Delbaere K. (2016). Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different. Med Biol Eng Comput 54: 663. doi:10.1007/s11517-015-1357-9 https://link.springer.com/article/10.1007/s11517-015-1357-9

About the Author

The Prevention of Falls Network for Dissemination (ProFouND www.profound.eu.com) is a thematic network of 20 partners and 14 associate members across Europe. ProFouND organises a yearly Falls Festival to discuss pressing topics in relation to falls in older people. Falls and injurious falls represent a major public health challenge for European countries, and across the world, not in the least due to a high associated cost. Falls cost about 1-1.5% of national health care expenditure.

Despite an increasing amount of evidence regarding programmes that work and programmes that do not work, there is wide disparity in fall prevention across EU and the world. Some regions are running ambitious programmes, whilst others lag behind. ProFouND, EU Falls Festival Scientific Committee, European Innovation Partnership on Active and Healthy Ageing Action Group on Falls and E-NO FALLS working groups wrote a Silver Paper[i] to address this and suggest ways of how research can help to close the implementation gap in falls prevention.

There is sufficiently strong evidence of what works to create best practice models. The challenge remains how these models can be implemented coherently and comprehensively. The EC Blueprint on Digital Health and Care Innovation for Europe’s Ageing Society[ii] argues the need for models of self-organisation and citizen empowerment for social transformation facilitated by digital and technological innovation. However, in order to have successful self-management, more potential barriers need to be conquered. For example, the challenge with evidence based strength and balance programmes (for groups and for individuals at home) is that they need to be attractive to older people so that they not only start the programme but also adhere to them long term to be beneficial.

Figure. Self-management of falls prevention through the use of exergames

Technologies can help facilitate the implementation of such strategies, but they must be attractive to older people[iii]. Technologies need to be developed for the prediction, detection, assessment and prevention of falls, which provide alerts and feedback useful to the multiple stakeholders, including health and social care professionals, whilst prioritising older people and their families and taking account of older people’s needs and preferences for technologies[iv]. We are following the blog with great interest and see exciting research from the ISPGR research community coming our way, showing great promise in making this happen. Keep up the good work!

Events: www.eufallsfest.eu

About the Author

Recent advances in body-worn sensor technology make it possible to objectively measure real-world fall events. These exciting new developments in research areas of software engineering, biomechanics and big data management can improve our understanding of fall events in older people. However, there is one problem: These events are rare and hence challenging to capture. This has been the motivation behind the EU-funded FARSEEING consortium, who, together with associated partners, have started building a meta-database of real-world falls.

Between January 2012 and December 2015, a large number of real-world fall events measured by inertial sensors have been reported to the database. A signal processing and fall verification procedure has been developed and applied to the data. Currently, more than 200 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % also include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals.

Figure 3 shows a real-world fall signal example (acceleration) with labeled activities and fall phases. The sensor (Samsung Galaxy S3) was attached at the lower back, sampling at 100 Hz. The faller reported a backwards fall while pushing the door opener. The person was upright at the beginning, indicated by the vertical axis (blue) showing 10 m/s², including some walking. During the fall the vertical signal changes to 0 m/s² and the anterior-posterior axis (red) to 10 m/s², indicating a backward fall. After a short period of resting, the person recovered with an intermediate resting position and continued walking.

This meta-database is currently the largest collection of real-world falls using inertial sensors. It will help to substantially improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection. The FARSEEING consortium aims to share the falls data with other researchers. A dataset of 20 selected fall events is available on request via the project website. Researchers are also invited to collaborate with the FARSEEING consortium on specific research questions.

More information about the project and the data sharing policy can be found on the FARSEEING website (www.farseeingresearch.eu).

Publication

Klenk J, Schwickert L, Palmerini L, Mellone S, Bourke A, Ihlen EAF, Kerse N, Hauer K, Pijnappels M, Synofzik M, Srulijes K, Maetzler W, Helbostad JL, Zijlstra W, Aminian K, Todd C, Chiari L, Becker C. The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. European Review of Aging and Physical Activity. 2016;13:8.

http://https://eurapa.biomedcentral.com/articles/10.1186/s11556-016-0168-9

About the Author

Wearable sensors have become an important research tool because they can be used to monitor physical movement remotely. This makes it possible to estimate how much energy a person expends when they perform their daily activities and can help in the management of certain chronic diseases (e.g., diabetes and obesity) which are often managed through increasing regular physical activity levels. In this scenario, the accuracy of an activity classification algorithm becomes pivotal because it directly influences the estimate of a person’s energy expenditure. The current study aimed to compare the accuracy of an activity classification algorithm that is trained on younger adults and evaluated with data collected from older adults and vice versa.

Thirty seven older adults (83.9 ± 3.4 years) and twenty younger adults (21.9 ± 1.65 years) were asked to perform several activities of daily living under semi-supervised free-living conditions whilst carrying a smartphone in their pants pocket. The measurements from the smartphone’s sensors (accelerometer, gyroscope, and barometric air pressure sensor) were recorded whilst participants walked (both on level ground as well as upstairs and downstairs), remained stationary (i.e., standing, siting and lying) or transitioned between different sedentary positions. The accuracy of the activity classification algorithm developed was lower when the algorithm was trained on the data collected from the younger cohort and tested on the data collected from the older cohort, particularly when trying to identify periods of stair ascent and descent. When the mean value of the differential pressure feature (see Figure 1.) across the younger cohort was compared to the mean value across the older cohort, the magnitude of the value was significantly smaller (p< 0.001) which suggests that the older adults ascended and descended the staircases at a slower rate compared to the younger adults.

The study shows that the accuracy of an activity classification algorithm is dependent on the characteristics of the cohort on which it is trained. This is an important consideration to keep in mind, especially when using the same algorithm across different age groups, or in people with different physical characteristics due to certain clinical conditions (e.g., Multiple sclerosis, Parkinson’s disease). Future algorithm development for activity classifications should focus on fine-tuning algorithms based on the populations in which it will be used.

Publication

Del Rosario, M., Wang, K., Wang, J., Liu, Y., Brodie, M., Delbaere, K., Lovell, N., Lord, S. and Redmond, S. (2014). A comparison of activity classification in younger and older cohorts using a smartphone. Physiol. Meas., 35(11), pp.2269-2286.(http://iopscience.iop.org/article/10.1088/0967-3334/35/11/2269)

About the Author