1. Introduction
Stroke occurs when there is lack of blood supply to the brain and can cause a wide variety of physical, psychological and cognitive problems, leading to long-term disability [
1]. According to the World Stroke Organisation, over 13 million people have a stroke each year (
https://www.world-stroke.org/, accessed on 26 May 2022). In the UK alone, there are estimated to be around 1.3 million stroke survivors, with instances potentially increasing 60% from 2015 to 2035 [
2]. The financial burden of stroke rehabilitation on healthcare services is also rising rapidly, which, combined with the problem of staff shortages (
https://nhsfunding.info/symptoms/10-effects-of-underfunding/staff-shortages, accessed on 26 May 2022), increases the need to consider technological solutions for supporting people with stroke.
Robotics has emerged as a promising beacon in physical assistance to address service shortage and support healthcare workers in the care and rehabilitation of people with stroke. Mobility assistive robotic devices include (but are not restricted to) smart walkers [
3] and sit-to-stand (STS) aids [
4], which help people who need physical assistance to perform activities of daily living (ADLs). ADLs are defined as self-care tasks, such as bathing, dressing, toileting, transferring, and feeding [
5]. Transfers such as getting in and out of bed, chair or toilet involve STS.
STS activities are thus key for independent living, and are performed around 60 times per day in healthy adults [
6], enabling functional independence for activities such as walking and toileting. However, transfers (including STS) are the most common cause of falls in people with stroke [
7], with STS activities presenting a higher risk of falls for those with impaired strength, balance or posture [
8]. Falls are a major hazard for people with stroke, causing injury, lengthening hospitalisations and impacting rehabilitation [
9]. Therefore, minimising the risk of falls during these actions is crucial in the creation of assistive devices for stroke rehabilitation.
The ultimate goal of this research is to provide the basis for the development of personalized home assistance for people with stroke during STS transfers, promoting their independence and rehabilitation. Existing manual handling assistive equipment, such as slings and hoists, require constant physical support from the carer to the patient using the device, resulting in increasing work-related musculoskeletal disorders, a major safety concern in today’s healthcare environment [
10]. Moreover, the use of a non-perfect fitting device can be uncomfortable or even dangerous, e.g., if it is too large, the patient might slip through.
More advanced robotic solutions have been studied to assist with STS transfer, such as the ballbot [
4], a single-wheeled mobile robot that can help a user stand with the aid of two arms that can be held onto during the STS motion. Although the ballbot can apply varying levels of assistance, its maximum pulling force is quite low, and it does not have any functionality for preventing falls, limiting its use with more vulnerable users. Moreover, this robot creates the same profile for all users and is less adaptable to individual needs. Another example is ROBEAR (
https://www.riken.jp/en/news_pubs/research_news/pr/2015/20150223_2/, accessed on 26 May 2022), a bear-shaped robot aiding with both STS transfers and rising from bed. ROBEAR, only a prototype, does not have any adaptive capabilities for users of varied ability levels and requires supervision from healthcare workers, limiting its use for maintaining independence. Exoskeletons have also been explored for STS assistance, with some small-scale studies showing that such devices can reduce load on a users’ muscles during STS movements [
11,
12]. Because these devices are only affixed to the lower body, they benefit from not requiring a user to grip onto handles during STS, but conversely, this creates added risk from the upper body not being supported. Moreover, being “permanently” attached to the participant, exoskeletons may not prove comfortable for everyday use.
One of the main issues with the implementation of these devices into practice is the lack of adaptability. Adaptability in assistive robots is a key enabler, as behaviours can vary between patients who may also progress or regress in their rehabilitation [
13]. The assistance provided by the robot should therefore adapt to these individual needs. Failure to do so can result in weakness of the user when too much assistance is provided, or reduced motivation when too little support is given and the person is unable to complete the task. Moreover, an adaptive strategy enhances intuitiveness and natural use of the device, endowing the robot with partial autonomy and unloading the user and/or carer from the burden of regulating the device. To tackle robot adaptability, this project focuses on two main objectives: (i) using biomechanical analysis to understand different motor patterns of sit-to-stand and stand-to-sit movements in individuals with and without stroke, and (ii) trajectory prediction for STS focusing on informing a user-centric adaptive control approach for assistive robots.
To capture full-body biomechanical data, it is possible to use vision-based motion capture systems, such as Microsoft’s Kinect (
https://azure.microsoft.com/en-us/services/kinect-dk/, accessed on 26 May 2022). Previous work has used these systems to great effect, particularly when motion is constrained to a single plane [
14]. It also shows high reliability for gait analysis [
15]. However, vision-based systems present several issues, such as the underestimation of joint angles [
16] and larger errors when estimating unconventional positions [
17]. Thus, although such systems are lower in cost than IMU-based systems, their potential inaccuracy as well as their often lower capture rate reduces their ability for use in clinical trials. Taking into account these factors, we decided to use more precise IMU-based recordings for the creation of the dataset, while we considered that due to cost-effectiveness and usability issues (people with disabilities might have issues using wearable devices), the developed assistive device would use a machine-vision-based system.
Biomechanical analysis of STS movements has been carried out for many years, with studies focusing on a wide variety of factors often combining adjustments to experimental parameters, such as seat heights [
18,
19] or allowing arm use for momentum [
20]; with measuring biomechanical factors, such as angles and moments of individual joints [
18,
21] or body weight distribution at points of contact [
22]. However, these studies often only capture or provide partial data of the body mechanics. For example, in [
21], pressure data were recorded for participants’ feet but not from under the seat, giving an incomplete picture of how weight is distributed and transferred during STS movements. Authors in [
19] simplified the human model by negating the participants’ arms for ease of calculation, which then loses balancing and momentum gaining forces generated by the upper limbs. In works such as [
19,
21,
22] among others, the authors enforced strict rules about how the participants should sit and perform the STS movements, which does aid the consistency of findings but fundamentally fails to recreate how people perform STS actions in their everyday lives. Moreover, the data from these studies are usually difficult to retrieve and proprietary, and to the authors’ knowledge, no open-source database containing full biomechanical data for sit-to-stand-to-sit (STSTS) is currently available. Some datasets do exist but are quite limited, such as only using one accelerometer to measure trajectories [
23], solely measuring force data from a balance board [
24], or using unnatural or ‘perturbed’ motions [
25]. Also uncommon are datasets containing data from users with pathological conditions [
26]. Another limitation of many existing studies is the focus on sit-to-stand without including stand-to-sit, an equally important transfer. Stand-to-sit may introduce extra risks or complications to the movement trajectory of an impaired person due to the fact that gravity is acting with the participant, requiring additional muscle control from the subject to sit down safely [
27]. By capturing the full biomechanical data alongside pressure data on both the seat and floor, for all multiple seat heights, sit-to-stand as well as stand-to-sit movements, and for both stroke and non-stroke participants, this study aims to create a comprehensive open-source dataset available for future research in this field.
Based on these recorded data, a trajectory prediction algorithm can be applied to retrieve the estimated motion of a user. However, though trajectory prediction has been widely documented for walking [
26], it is very scarce for STSTS movements. Previous works such as [
28], have used cost functions to generate trajectories for different groups of individuals and even applied them to an assistive device achieving high success rates [
29]. In [
28], full-body motion capture in addition to force data from sensors under the feet and seat were used to calculate trajectories. However, the participants had to stand up from a bicycle seat which would be sat on and stood up from in a different way to a traditional chair; therefore, the trajectories recorded might not necessarily reflect how a participant may move in their everyday life. Similarly to the current study, in [
29] an Xsens MVN motion capture suit was used to monitor healthy participants with high accuracy and frame rate for their unimpaired subjects. For the older subjects, however, an optical marker system was used to capture biomechanical data, and this inconsistency may have created extra variations in the data from the two participant groups. Due to the task at hand, where the sample space for participant heights and weights was fairly small, the small distances between datapoints allows algorithms, such as weighted
k-nearest neighbours (
) [
30], to generate accurate results.
The aim of the proposed study in this manuscript is thus to evaluate whether simple machine learning algorithms such as can be used to predict STSTS trajectories to inform adaptive robotic control strategies for people with stroke.
4. Discussion
This study used a
and linear regression model to produce trajectories for unseen participants during sit-to-stand and stand-to-sit movements, which in most cases only deviated from the true path by a few centimetres (see
Table 2 and
Table 3). This was most accurate for subjects near to average heights and weights, as their nearest neighbours were more likely to be a lot closer than for subjects on the fringes of the dataset. This model was able to recreate the full mid-shoulder trajectories from non-stroke subjects with an average
score of 0.864 ± 0.134 for sit-to-stand movements starting from 100% seat height and using height and weight as
coordinates, in most cases deviating from the true trajectory by no more than 5 cm. For 115% seat height, though we still had achieved some level of prediction, the
score decreased and error increased for both sit-to-stand and stand-to-sit movements. This could be due to a raised seat height being less familiar to participants, which as a result created more variation in how they distributed their weight and generated momentum, changing their STS patterns. Additionally, balance could have played a role in the repeatability of the motions, as at 115% height, the seating position was less stable and the impulse to stand up could have been less predictable. A larger number of participants would increase the accuracy of the model, and is an aim for the future of this project.
Comparing the use of height and weight, and age and BMI as
coordinates, the former provided the highest single score, but only by a very narrow margin, whereas age and BMI was shown to more successful for stand-to-sit trials, including having a generalized smaller error. Age and BMI was also more successful at predicting trajectories for higher values of
k, which can avoid chances of overfitting [
45]. For these reasons, our results suggest that using age and BMI as
parameters is more successful for predicting STSTS movements.
After validating the prediction model, it was also used to estimate ‘ideal’ trajectories for stroke subjects, which could be used for the control of an assistive devices. As it can be observed in
Figure 8 and
Table 4 and
Table 5, the difference between ‘ideal’ and real trajectories varied depending on the subject. For example, subject
exhibited a deep lowering behaviour at around 40% of the movement, that reflected the impulse taken during the sit-to-stand and that is not present in an ‘ideal’ trajectory. Thus, a control could be envisioned in which the robot tries to follow such ‘ideal’ trajectory depending on the level of impairment and assistance needed by the subject. Conversely, participant
was in a good physical state and had little level of impairment, which was visible from the small difference between the the ‘ideal’ and measured trajectories. Thus, in this case, the robot would provide little assistance to the person’s movement.
Table 4 and
Table 5 shows that using age and BMI as
coordinates results in higher average
values across all movements, and therefore is more appropriate for use in future work.
Moreover, in this work, we managed to create a comprehensive data-set of full-body biomechanics and force data for 30 participants, including 6 who had experienced stroke. These data allowed for a first comparison analysis of body motion for people with and without stroke during STSTS movements, and opens the door for further research on physiological studies and robotic assistance.
Based on the results shown in
Figure 5, the range of displacement during sit-to-stand, particularly in the
x-direction of the CoP, could be used as an indicator of potential falls, and utilized by an assistive robot to take preventive actions. Despite being able to see large differences in body position and applied forces (
Figure 8) for some stroke subjects, the small sample size of stroke participants meant that quantitative analysis of these differences could not be performed reliably. However, the large range of ability levels within the stroke subjects group highlights the importance of future assistive devices to be adaptive to the individual user, and thus to have personalized predictions of their movements.
It must be also pointed out the difference in ages between the two groups, with averages of 37.2 ± 12 years for non-stroke and 66.5 ± 10.7 years for stroke participants. Although some non-stroke participants tested were in their fifties and sixties, the majority were much younger than participants in the stroke group. This could mean that some of the biomechanical differences seen in the stroke group were influenced by age as well as the effects of the stroke. Further research will expand the number of stroke participants tested, including younger people with stroke, to reduce this age discrepancy.
Other trends noted in the stroke-affected participants included a ‘rocking back’ motion when preparing to stand, seen both in the seating mat data with a higher weight applied just before standing,
Figure 6, and also mirrored in the balance board data,
Figure 4, where participants removed some weight from the feet just before acting. Additionally, some stroke participants exhibited a large difference between weight applied to each foot during STSTS motions, highlighting favouritism of their unimpaired limb and potential increased risk of falls. A key observation is that these trends were not homogeneous among the groups, with some stroke participants showing very little difference between weight applied to each foot, with some non-stroke participants also showing some favouritism of one leg. Larger sample sizes would help to determine more definitive trends within these groups, and create segmentation within groups further than just ‘stroke’ and ‘non-stroke’.
It is possible that some of the stroke-affected participants may use compensatory behaviours, such as pushing off the arms of a chair when performing STS movements in their everyday lives [
36,
46]. Prohibiting these behaviours in this study may have affected how the participants performed STSTS movements, but care was taken to ensure that all participants were able to safely perform STSTS movements independently.
People with stroke demonstrate considerable asymmetry of weight distribution during sit to stand, demonstrating significantly increased weight bearing on the unaffected side [
47,
48]. Stroke survivors also commonly exhibit a reduced peak vertical reaction force, an increased time to complete the movement of sit to stand and a larger medio-lateral centre of pressure displacement compared with healthy adults [
47]. This also can be seen when looking at the data from the force sensors in
Figure 4; the non-stroke participant spreads the weight much more evenly over their two legs, and it is clear that they favour weighting one leg throughout the whole movement, which is also shown in the higher weight distribution imbalance for stroke participants using Equation (
9). The non-stroke person sits much further back on the seating mat than the person with stroke in
Figure 6, and it has been shown that sitting or ‘scooting’ forward on a chair can make performing STS movements easier for elderly people [
46,
49], thus showing that the non-stroke user is able to apply more torque to their legs. Comparing the first frame of the stroke patient with the second and third frames, more weight is applied to the seating mat just before the patient stands, representing the patient ‘rocking’ backwards to build momentum for the stand movement. This was also observed visually during the trials.
The data created in this research are a valuable starting point for further designs of assistive robots. We demonstrated that simple machine learning algorithms are able to predict STSTS movement trajectories with relative accuracy, which could be used as inputs for a robot aiming to follow a user’s STSTS trajectory. We highlighted areas where true trajectories differ from predictions, which a robot could use to adapt the level of assistance it is providing to guide the user back to a safe path. We showed that impaired participants tend to exhibit more unstable forces when standing, which could be used as indicators of potential falls and trigger a robot to take preventative measures. Building upon previous datasets found in literature [
23,
24,
25], this dataset combines full-body biomechanics with force data from multiple sensors, allowing for more thorough analysis and comparison between sensors. Further exploration of this dataset could involve analysing participant velocities and accelerations to determine the maximum forces that a STSTS assistive robot would need to be able to apply to operate safely for a wide variety of users. Another avenue could be to focus on a participants’ movement in the horizontal plane and their centre of pressure, as a robot may attempt to prevent falls by reducing movement in these parameters.
A limitation of this study is that it focused on stroke participants who were able to stand safely by themselves to minimize the risk of falls or injury. Stroke participants were recruited from within the Bristol After Stroke charity, which is based in the community rather than in hospital or other rehabilitation settings, and therefore supports more able patients. This means the dataset does not include trajectories of persons severely physically impaired by stroke and therefore does not show the likely larger variation in trajectories, body positions and applied forces that these subjects might demonstrate.
However, a STSTS assistive robot is likely to target a wider range of impaired subjects, possibly including those who might be unable to stand independently. Further research should focus on testing and recording these user’s biomechanical data when given the minimum assistance required to allow them to perform STSTS movements. These recordings would help to determine physical additional requirements of a STSTS robot, such as workspace, force limits, and safety measures, as more severely impaired users are likely to exhibit more extreme behaviours. Measuring this variety of movement patterns will be useful for the safety testing and validation of such robots. In addition to a wider variety of user ability levels, future work could investigate more variability in STSTS scenarios, such as rising from lowered seats, which, although are not ideally suited for impaired users, are still situations that may arise in a home environment.