**Functional Evaluation Using Inertial Measurement of Back School Therapy in Lower Back Pain**

**Claudia Celletti 1, Roberta Mollica 1, Cristina Ferrario 2,3, Manuela Galli <sup>3</sup> and Filippo Camerota 1,\***


Received: 4 December 2019; Accepted: 16 January 2020; Published: 18 January 2020

**Abstract:** Lower back pain is an extremely common health problem and globally causes more disability than any other condition. Among other rehabilitation approaches, back schools are interventions comprising both an educational component and exercises. Normally, the main outcome evaluated is pain reduction. The aim of this study was to evaluate not only the efficacy of back school therapy in reducing pain, but also the functional improvement. Patients with lower back pain were clinically and functionally evaluated; in particular, the timed "up and go" test with inertial movement sensor was studied before and after back school therapy. Forty-four patients completed the program, and the results showed not only a reduction of pain, but also an improvement in several parameters of the timed up and go test, especially in temporal parameters (namely duration and velocity). The application of the inertial sensor measurement in evaluating functional aspects seems to be useful and promising in assessing the aspects that are not strictly correlated to the specific pathology, as well as in rehabilitation management.

**Keywords:** back school; inertial sensor; lower back pain; rehabilitation; stability; timed up and go test

#### **1. Introduction**

Lower back Pain (LBP) is a well described and extremely widespread health problem [1]. LBP is a pain that goes from the twelfth rib to the lower gluteal folds; pain can also spread to the lower limbs for one day or more [1]. This condition is the main cause of absence from work and activity limitations in much of the world. The consequence is a heavy economic burden for subjects, families, communities, industry, and governments [2]. Of the 291 conditions studied in the 2010 Global Burden of Disease (GBD) report, LBP had the highest load. LBP is the leading cause of disability globally [3].

The main components to treat this condition are education, reassurance, analgesic drugs, and non-pharmacological therapies. During the treatment, periodic check-ups are recommended based on individual patient needs, such as prognosis, treatment prescribed, and remaining concerns about serious pathological abnormality [4].

Chronic LBP is defined as lower back pain that lasts for over 12 weeks. Generally, one-third of the patients with LBP reported that in the year after an acute episode, lower back pain was of moderate intensity [2]. In patients with chronic back pain, a multidisciplinary approach leads to better results when combined with medical, rehabilitative, and psychological treatments [5].

Among other rehabilitation approaches, back schools (BS) are interventions that comprise an education component and exercises. BS are training programs with lessons given by a therapist to patients or workers, with the aim of treating or preventing lower back pain [6]. Several studies have demonstrated the efficacy of BS in reducing and managing lower back pain [7]. BS, due to the validity of their educational exercises, enhance the quality of life, reduce disability induced by LBP [8,9], and also improve mental well-being.

The aim of this study is to evaluate not only the efficacy of BS therapy in reducing pain but also in functional improvement, an aspect strictly related to pain but normally not evaluated in the studies that focus on assessing pain relief. A new and simple gait evaluation method is used to make the analysis. In particular, stability and ability to perform functional tests, such as the timed "up and go" test, are evaluated in order to verify if a rehabilitation program based on BS therapy is able to improve stability and walking.

#### **2. Materials and Methods**

Patients were recruited from the Rehabilitation Ambulatory Service of Umberto I University Hospital. All participants signed informed consent forms after receiving detailed information about the study's aims and procedures for the Declaration of Helsinki.

#### *2.1. Eligibility Criteria*

Patients were included in the study if they had lower back pain that had lasted for more than six weeks that was associated with limitations of motion. The presence of vertebral infections; tumoral metastasis; fractures and neoplasm; rheumatological, neurological, or oncological disease; previous back surgery; severe cognitive impairments; or pregnancy was considered an exclusion criterion.

#### *2.2. Intervention*

The BS program was supervised by a multidisciplinary professional team. A total of 10 one-hour sessions scheduled 3 times a week were carried out. The adopted rehabilitation program was chosen by considering the effectiveness of the BS on LBP reported in previous studies. The details of the program followed in this study are described below.

The first treatment session was used to provide subjects with basic anatomical knowledge of the spine and its functions; the correct ergonomic positions to be maintained in everyday life were also shown. During the following 9 sessions, the physiotherapists supervised the activities, which consisted of exercises based on diaphragmatic breathing (10 min), self-stretching of the trunk muscles (10 min), strengthening of erector muscles of the spine, abdominal strengthening, and postural exercises. The tasks were divided into 3 sets of 10 repetitions for each one; 3 min of rest was provided between each series. Explanations of the ergonomic position of the spine and how to introduce self-correction in daily life were provided for the whole duration of the treatment.

#### *2.3. Health State: Clinical Evaluations*

Patients were evaluated before and after physiotherapy treatment with the following clinical scales:


4. The *timed up and go test* (TUG) is a clinical test that evaluates the balance and mobility of a subject [14,15]. In the traditional TUG test, a stopwatch is used to measure how long it takes a subject to lift off a chair, walk 3 m, turn 180◦, return to the chair, and sit back down.

#### *2.4. Biomechanical Evaluation*

#### Instrumentation

In this study, we evaluated the TUG as both a time test and also using an inertial measurement unit (IMU). The commercial name of the device used is a G-Sensor instrument (BTS SpA, Milan, Italy). The communication with the receiving unit (personal computer) takes place via a Bluetooth connection. The associated software (BTS® G-Studio) is used to acquire, process, and archive data. In the IMU there is a triaxial accelerometer (16 bits/axes, up to 1000 Hz) with different sensitivities (±2, ±4, ±8, ±16 g), a triaxial 16-bit magnetometer (±1200 μT, up to 100 Hz), and a triaxial gyroscope (16 bits/axes, up to 8000 Hz) with multiple sensitivities (±250, ±500, ±1000, ±2000◦/s). The G-Sensor is positioned at level L5 using an elastic belt. It is important to keep the power connector facing upwards and the logo outwards to correctly define the reference system (Figure 1a)

**Figure 1.** (**a**) Inertial measurement unit (IMU) position and (**b**) timed up and go test (TUG) phases.

The test begins with patients seated in a standard chair with their arms on either side of their body. After a signal from the clinician, the subject rises from the chair, walks three meters in a straight line at a speed that is normal for them, turns around an obstacle, and finally returns to the chair and sits down. The software used is BTS G-Studio, which has a specific protocol capable of analyzing the TUG test and automatically generates a TUG report with temporal parameters identifying the duration of the different sub-phases [16]. The mathematical method used to identify each sub-phase is the one described in the study by Salarian et al. [17]. Additionally, a detailed description of the practical operation of BTS G-Studio in iTUG analysis, as compared with an optoelectronic system, is provided in the study by Negrini [18].The test can, therefore, be divided into different phases: the first is that of rising from the chair (sit-to-stand sub-phase), walking for 3 m until reaching an obstacle (walking forward sub-phase), turning around the cone (mid-turning sub-phase), walking three m back towards the chair (return walking sub-phase), and then turning and sitting down on the chair (stand-to-sit sub-phase) without using the assistance of their arms, if possible. The test is concluded when the subject is seated again. The final report of the TUG test shows all the spatiotemporal parameters related to the walk for each sub-phase considered: the sit-to-stand, the steady-state gait, the turning, and the turn-to-sit phases [17]. The parameters supplied automatically by the IMU for each trial are: total time duration, sub-phase durations, mean velocity turning (mid-turning and final turning sub-phases), and the maximum trunk flexion angle and its range of motion during sit-to-stand and stand-to-sit sub-phases (Figure 1b).

Furthermore, an instrumental evaluation of stability was carried out using a baropodometric platform (P-Walk BTS Engineering). The stabilometry test measures the oscillations by evaluating the elliptical area containing 95% of sway points, velocities with closed eyes (CE) and opened eyes (OE), and the length of the excursion of the center of pressure. The test we performed had a duration of 30 s, within which the position of the CoP was recorded during quiet standing [19]. Patients were adequately informed about the procedure; the requirements were to maintain a natural standing position with the arms alongside the body, the feet open at an angle of about 30◦, and the heels at a distance of about 3 cm. All tests were performed by the same examiner in order to reduce the inter-operator error and to increase the reproducibility of the test; thus, the subjects were given the same information before each test. For each trial condition (EO and EC), three tests were carried out, for which the median scores are reported. Considering the EO condition, subjects were required to stare at a mark fixed at eye level on a wall 1.5 m away.

#### *2.5. Statistical Analysis*

The statistical analysis was performed with SPSS software. To verify the normality of the parameters, the Kolmogorov–Smirnov test was used. When the normality assumption was not fulfilled, the median and range (minimum–maximum) were evaluated. The differences between variables were evaluated using the Friedman test for paired samples. The probability level for statistical significance in all tests was set at a *p* < 0.05.

#### **3. Results**

Forty-eight patients (mean age 71 ± 13.66) were recruited for this study; 4 patients did not complete the rehabilitation program and were excluded from the study; a total of 44 patients (34 female and 10 male, mean age 70 ± 14.02) were evaluated before and after back school treatment.

We observed a global pain reduction in patients with LBP that attended the back-school program. This reduction was also associated with clinical improvement of stability, as shown by the POMA balance score increase. When the postural analysis data were examined, a variation was not registered when considering the opened eyes test; instead, in the closed eyes test a significant reduction of the length of CoP was registered (Table 1).


**Table 1.** Clinical scale and instrumental evaluation before and after back school cycle.

Legend: POMA = performance-oriented mobility assessment; NRS = numeric rating scale; ODI = Oswestry disability index; OE = opened eyes; CE = closed eyes; TUG = timed up and go; s = second.

It is interesting to notice that there was a significant reduction of the total duration of the TUG test, and also of the stand-up and sitting phases (Table 1).

The BS groups showed significant improvement in several instrumental TUG (iTUG) parameters, especially in temporal (duration and velocity) parameters.

The BS treatment significantly reduced the total duration of the task and its sub-phases: the stand-to-sit sub-phase and the sit-to-stand phase, the mean velocity of TUG, and of mid-turning and final turning sub-phases increased at a significant level.

#### **4. Discussion**

As far as we know, this is the first paper to evaluate not only the pain aspect of lower back syndrome after treatment, but also the functional aspect that is not strictly related to this pathology (i.e., timed up and go evaluation). The TUG test provided in this study is an instrumented TUG. While the TUG test taken by an expert operator using a stopwatch has excellent reliability, accuracy, and precision, this measure is subjective and operator-dependent (i.e., a less experienced clinician could affect the quality of the measure). The use of the stopwatch in the clinical setting has several limitations: (a) the identification of the start time and the end time are not easily detectable by the operator; (b) the evaluation of the TUG time requires a high level of attention by the operator, which could decrease when many trials are required; (c) the quantification of sub-phases is not possible.

The instrumented TUG analysis is of considerable interest, as it evaluates the various sub-phases of the test (chair transition, straight-ahead gait, and 180◦ turn); this allows a better understanding of movement strategies. Considering, for example, the 180◦ turn, there is a variability between subjects with different gaits and with **or** without balance impairment. A further variation is introduced for patients using an assistive device, such as a walker.

Therefore, the IMU technology implementations for the iTUG quantification of pre- and post- specific therapies have several benefits, including additional performance parameters, generation of reports, fast assessment, and that the patient does not need to be undressed. In addition to this, it is important to consider the ability for self-administration at home and in a clinical environment. This could provide more details and insights about patient performance [16]. Although other variables could have been derived using the data provided by the wearable sensor, as the purpose of this work was to analyze the TUG, which is an automatic functional clinical test, the analysis focused mainly on the evaluation of the duration of the task included in the test. It is known that lower back pain is associated with functional impairment. In particular, the opportunity to analyze the different phases of this test using an inertial measurement instrument made it possible to assert that back school therapy may improve back function, increasing the promptness to position changes and speeding up movements. The changes observed with iTUG represent the effect of the reduction of LBP on functional ability. As the patients experience pain during the movement, the biomechanical result is a slow movement and a higher TUG time. After treatment, the patients feel better, experience less pain, and can get out of the chair faster. No changes are evidenced as far as postural acquisition is concerned. In maintaining postural control, pain in the lumbar area has a minor effect in terms of functional limitation, and therefore one can expect to have no obvious variations in postural control.

#### **5. Conclusions**

In conclusion, through the quantitative evaluation of the iTUG test, it is proven that the BS could be considered a promising new rehabilitative treatment for LBP in improving motor functional limitations. Moreover, as the IMU sensor can provide data that might provide many more temporal and kinematic measures after successive elaboration, future development of this study should provide additional data for a more detailed analysis, in order to show more important changes in patients' movement patterns after the treatment.

**Author Contributions:** Conceptualization, C.C. and F.C.; methodology, C.C., F.C., and R.M.; formal analysis, C.C., M.G., and C.F.; investigation, C.C. and F.C.; data curation, C.C. and F.C.; writing—original draft preparation, C.C.; writing—review and editing, F.C., M.G., and C.F.; supervision, R.M. and M.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We thanks Daniele Scilimati and Laura Venerucci for their contributions in evaluating and treating patients.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson's Disease**

**Nooshin Haji Ghassemi 1,\*, Julius Hannink 1, Nils Roth 1, Heiko Gaßner 2, Franz Marxreiter 2, Jochen Klucken <sup>2</sup> and Björn M. Eskofier <sup>1</sup>**


Received: 23 May 2019; Accepted: 9 July 2019; Published: 13 July 2019

**Abstract:** Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson's disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians' gait assessment and to monitor patients in their daily environment.

**Keywords:** Parkinson's disease; pathological gait; turning analysis; wearable sensors; mobile gait analysis

#### **1. Introduction**

Gait is an important part of mobility that is impaired in neurodegenerative diseases like Parkinson's disease (PD). As the disease progresses, gait fluctuations become more severe. Different locomotor patterns in gait, such as straight walking and turning, require different levels of functioning and coordination. For a person with impaired mobility caused, for example, by PD, turning is challenging and potentially risky, even more than straight walking [1,2]. There have been attempts to identify and characterize turning abnormalities in order to complement the physicians' assessment of pathological gait.

Studies showed that turning deficits are manifested in mild PD even when there are no signs of impairment in straight walking [3]. Difficulty while turning may lead to posture instability and, potentially, even falls [4,5]. Risk of falling is higher during turning compared with straight walking [4,5]. Furthermore, deterioration of motor function during turning can cause progressive episodes of freezing of gait (FoG) [6–8].

Some studies have attempted to utilize the definition of disease stages and motor impairments by UPDRS-III [9] and H&Y [10] clinical scores and objectively assess turning deficits [11–15]. Studies on spatio-temporal parameters quantifying turning have demonstrated decreased speed, longer duration of turning, and a larger number of strides as the disease progresses [3,16–18]. Postural stability also decreases during turning for PD patients in comparison to healthy controls, particularly during fast walking [19].

Outside the clinics and in the majority of standardized clinical tests, a gait sequence includes both straight walking and turning. In order to differentiate between them during the course of a gait, different definitions of turning have been presented in the literature. For example, turning was defined as the movement between two pre-defined points that indicated the initiation and termination of turning [5]. Salarian et al. [17] used mathematical modeling in order to isolate turns from the whole gait sequence. Spatio-temporal parameters extracted from individual strides are different in straight walking compared to turning. Many studies used characteristics and statistics of spatio-temporal gait parameters to define turning [3,6,20]. Without a standard turning definition, studies then presented some clinical validations to support their definitions—for example, they showed that turning parameters were correlated to the established clinical scores [3,6,20].

Gait and turning can be measured by a variety of systems—from accurate but stationary motion capture systems [19] to small wearable sensors [3,17]. The focus of this study is on wearable sensors, since they give the opportunity to perform long-term monitoring of PD patients. Sensor placement plays a crucial factor in designing wearable systems. Many turning studies place the sensors on the upper extremity [3,6,20]. One advantage is that turning is easily detectable in the sensor signals [17]. However, gait disturbances such as FoG cannot be detected clearly from sensors on the upper extremity. Such systems still need additional sensors on the lower extremity in order to quantify turning in terms of spatio-temporal parameters [3,17]. In contrast, sensors on the lower extremity and, in particular, on the shoe provide higher biomechanical resolutions. Panebianco et al. [21] examined different sensor locations and showed that as sensors get closer to the foot, higher accuracy for gait events and parameters can be obtained. Moreover, for long-term monitoring of patients, sensors integrated in the footwear are less obtrusive and stigmatizing.

In order to measure gait, we used wearable sensors mounted on the lateral side of the shoe. In order to isolate turning from gait, we used the statistics of spatio-temporal parameters. The goal of this study is to show the applicability of the system in the objective analysis of turning and to evaluate whether it confirms the findings of other studies. To this end, we first introduce our novel turning isolation algorithm targeting data from a standardized 4 × 10 m gait test measured with wearable sensors placed on the shoe. Then, we quantify the isolated turnings through several spatio-temporal parameters that proved to be effective in detecting pathological gait [22–24]. Through meticulous statistical analysis, we evaluate the turning abnormalities in a large PD cohort. The value of this objective turning assessment was clinically validated by the correlation of the turn-derived parameters to clinical scores, including motor impairment and disease stages in PD.

#### **2. Methods**

#### *2.1. Wearable Measurement System*

For our experiments, data was recorded with a Shimmer 2R/3 Inertial measurement unit (IMU) (Shimmer Sensing, Dublin, Ireland), measuring acceleration and angular velocity at 102.4 Hz. Each unit consisted of a tri-axial accelerometer (range Shimmer 2R: ±6 g, Shimmer 3: ±8 g) and a tri-axial gyroscope (range Shimmer 2R: ±500◦/s, Shimmer 3: ± 1000◦/s). The sensor units were mounted laterally on each shoe below the patient's ankle. The measurements from both feet were included in the experiments. Figure 1 shows the sensor placement on the shoe and the axes definition.

#### *2.2. Study Population*

We recruited 108 PD patients during their regular visit in the movement disorder outpatient center at the University Hospital Erlangen. Sporadic PD was defined according to the guidelines of the German Association for Neurology (DGN), which are similar to the UK PD Society Brain Bank criteria [25]. Patients had to be able to walk independently (H&Y < 4, UPDRS gait item < 3) [10,26]. All PD patients were clinically (UPDRS-III) and biomechanically (gait analysis) investigated in stable ON medication without the presence of clinically relevant motor fluctuations during the assessments. We had an exclusion criterion for a severe cognitive impairment. To obtain quantitative gait data from controls, we recruited 42 age-matched controls with no signs of PD and/or other motor impairments. With respect to age, height, and body-mass-index (BMI), PD and control cohorts were matched (see Table 1). Data regarding laterality of the disease can be found in Table 1, where the UPDRS sub-items of rigidity lower and upper extremities were reported. This data shows that patients affected on the right and left sides are almost equally represented in our cohort. Written informed consent was obtained from all participants (IRB-approval-No. 4208, 21.04.2010, IRB, Medical Faculty, Friedrich-Alexander University Erlangen-Nürnberg, Germany).

**Figure 1.** (**a**) Shimmer sensor placement and axes definition. (**b**) Definition of turning angle, stride length, path length, and swing width.


Participants walked freely at a comfortable, self-chosen speed in an obstacle-free and flat environment for 4 × 10 m. After each 10 m of straight walking, participants were instructed to turn 180◦ at a preferred direction.

#### *2.3. Turning Isolation*

The standardized 4 × 10 m walking included four straight gait bouts and three turnings in between each two straight bouts. The goal was to isolate the three turnings from the whole gait sequence. To this end, the gait sequence was segmented to individual strides semi-automatically [27,28]. These strides should then be categorized as straight walking, turning, and transitions between straight walking and turning. In order to differentiate between these categories, we used statistics of spatio-temporal parameters.

The change of azimuth between two successive mid-stances was defined as the turning angle between consecutive strides (see Figure 1). The absolute values of turning angles were considered since the sign of values only showed the direction of the turnings, which is not of importance in our analysis. Similarly to Mariani et al. [20], strides with turning angles larger than 20◦ were classified as turning.

In order to identify transition strides in a gait sequence [20], again, statistics over turning angles were used, since this parameter is the best indicator of spatial foot movement during turning (see Figure 1). The turning strides with angles larger than 20◦ were eliminated from the sequence. A gamma distribution was then fitted to the tuning angels from the rest of the strides. We chose gamma distribution due to the fact that the distribution is one-hand tailed, in a way that strides from straight walking mainly centers on the mean. The highest 10% of the distribution was classified as the transition if the strides were adjacent to the turning strides. In fact, the strides in the highest 10% of the distribution were considered as anomalies in the distribution of straight strides. For turning analysis, we only considered turning and transition strides.

#### *2.4. Turning Parameters*

After the turning isolation, we had three sets of strides related to three turns in the standardized test. We extracted spatio-temporal parameters from these strides based on the algorithms in previous works [20,22]. The algorithms for obtaining parameters from our wearable sensor-based system were validated previously using a gold standard, such as an optical motion capture system or instrumented walkway. To quantify turning, two sets of parameters were computed for each turning—per-stride parameters and global parameters per-turn.

For the first group, a set of parameters was extracted from each stride: stride time, path length (normalized on patient's height), stride length (normalized on patient's height), stride velocity, and swing width. In turning, it is very likely that a stride has a curved trajectory, rather than a straight line. In such cases, length of movement in the straight line between the beginning and end of a stride is measured as stride length. In addition, path length was introduced to measure curve length between the beginning and end of a stride (see Figure 1). All these parameters were calculated from mid-stance of a stride to the successive mid-stance.

For the global parameters, we calculated the number of strides and total duration per turn. This set of parameters measures characteristics of the whole turn.

#### *2.5. Statistical Analysis*

In order to determine whether parameters can distinguish between different groups (controls and three stages of disease (see Table 1)), we applied the one-way analysis of variance (ANOVA). When a significant difference was found, a post hoc analysis was performed using Bonferroni's test to obtain a pairwise comparison between the groups. The significance level was set at *p* < 0.05. For measuring effect sizes, *η*<sup>2</sup> was defined as the ratio of variability between groups to the total variation in the data that was used. Cutoff values for small, medium, and large effect sizes were set at 0.01, 0.06, and 0.14, respectively, according to Cohen [29]. Statistical analysis and parameter computations were performed using MATLAB R2015a.

#### **3. Results**

As the disease progresses, gait impairment associated with deteriorated mobility becomes more prevalent. In this section, we examined whether spatio-temporal parameters that characterize turning were able to reflect gait impairments.

Figures 2 and 3 show spatio-temporal parameters that are characteristic of turning for global and per-stride parameters, respectively. Clinical scores in PD studies determine the severity of gait impairment and disease stages: the H&Y, UPDRS-III score, and the UPDRS-III sub-items for gait and postural instability. Patients with different levels of disease severity (see Table 1) and controls were statistically compared using ANOVA, followed by Bonferroni's post hoc test.

**Figure 2.** Global parameters characterizing turning: number of strides per-turn and turning time were calculated for controls and PD patients grouped according to H&Y disease stage, UPDRS-III total score, and the single items, gait and postural instability of the UPDRS-III. Group data are displayed as mean ± SEM and were compared using one-way ANOVA followed by Bonferroni's post hoc test, where \* indicates *p* < 0.05.

As the disease progresses, stride velocity, path length, stride length, and swing width (per-stride parameters) decreases, and as a result, patients need more strides and time (global parameters) to complete a turn. This can be observed for all clinical scores, although the two sub-items of gait and postural instability are showing larger differences between stages of the disease. Stride time shows no clear change between different groups.

Global parameters showed that PD patients, in contrast to controls, need significantly more time and a larger number of strides to complete a turn (see Figure 2). Number of strides per turn, in particular, shows a significant difference between the control and even early stage of the disease for the UPDRS-III score and its two sub-items. Moreover, there are significant differences between stages of the disease in most comparisons. Per-stride parameters, except stride time, show a significant difference between the controls, mild, and severe stages of the disease for all clinical scores. Stride velocity, stride length, path length, and swing width are able to differentiate disease severity by means of all tested clinical scores (see Figure 3).

To quantify effect sizes, *η*<sup>2</sup> is reported in Table 2. The effect sizes range from small to large. The largest effect sizes are obtained consistently over all clinical scores with *p* < 0.001 by the global

parameters, number of strides per turn, and turning time. Path length showed consistently higher effect sizes than stride length, which suggests that it is a more meaningful parameter for estimation of spatial foot displacement in turning. The effect sizes of per-stride duration are very small.

**Figure 3.** Per-stride parameters characterizing turning: stride velocity, path length, stride length, and swing width were calculated for controls and PD patients who were grouped according to the H&Y disease stage, UPDRS-III score, and the single items, gait and postural instability, of the UPDRS-III. Group data are displayed as mean ± SEM and were compared using one-way ANOVA, followed by Bonferroni's post hoc test, where \* indicates *p* < 0.05.


**Table 2.** ANOVA test: *η*<sup>2</sup> values for different parameters and clinical scores. Values with \* correspond to *p* < 0.001. Bold font indicates values with strong effect sizes.

#### **4. Discussion**

The aim of the present study was to investigate whether an on-shoe, sensor-based gait analysis system reflected turning abnormalities and whether it could objectively complement physicians' gait assessments. To this end, we recruited 108 PD patients and 42 age-matched controls, and measured their gait during a 4 × 10 m walk by using our system. We then isolated the turnings from the whole gait sequence and quantified them using several spatio-temporal parameters. The parameters extracted using an on-shoe wearable system were previously validated against gold-standard systems, such as an optical motion capturing system [30] or instrumented walkway [22], and results indicated their technical validity. The clinical validation that followed turn quantification showed that turning parameters extracted using our measurement system and the turn isolation algorithm can effectively reflect gait abnormalities and be successfully used for the objective assessment of turning.

There have been many studies regarding turning analysis in PD [3,17,20]; yet, there is no unique way to define turning. Turning has been defined using mathematical modeling [17], statistics of spatio-temporal parameters [20], or the path between two pre-defined points [5]. One reason for these diverse turning definitions is that, basically, there is no standard way to determine the start and end of the turning. Common gold standards, such as motion-capture systems or videos, cannot provide a ground truth for turning. Since transitions between straight walking and turning happen gradually, it is inherently difficult to determine a specific start- and end-point for turning. A technical validation seems impractical with the usual gold standards. Nevertheless, a specific definition of turning, supported by some clinical validations that show its usability, can be an asset in objective gait assessment [3,17,20].

Turns can have different lengths, angles, and bases of support. We can expect that different types of turning require different levels of coordination [19]. In this study, we analyzed 180◦ during the 4 × 10 m walk test. Turnings with 180◦ were also analyzed in other standardized tests, like Timed Up and Go (TUG) [16,31,32]. We studied a 4 × 10 m walk because it includes three turns, which makes it statistically more meaningful to draw any general conclusions from the experiments. Regardless of the type of the turns, the underlying concepts that were used in this study are valid, although the turning isolation algorithm may need some adjustments to distinguish between straight walking and turning in an optimal way.

The findings of this study confirm the results from other studies [11–15], showing that spatiotemporal parameters can manifest gait deficits even in early stages of the disease. Results show that as the total duration of a turn increases, the stride length and velocity decreases and more strides are needed to complete a turn in the PD population. Such changes in parameters were scaled with PD severity. Global parameters of turning, such as the number of strides per turn and the total duration of the turn, can distinguish different groups. This is an important finding for PD studies, because gait problems are difficult to detect by physicians in early stages of the disease, whereas sensor signals can capture subtle differences between a healthy and abnormal gait in the early stages of the disease. The large effect sizes for global parameters further emphasized the efficiency of these parameters for yielding statistical differences between different groups. Previous studies showed similar results for such global turning parameters [3,16,17]. Per-stride parameters of stride velocity, path length, stride length, and swing width can distinguish the majority of groups, although to a lesser extent in contrast to global parameters. For example, the distinction between controls and early-stage PD patients is more effective in global parameters. Furthermore, the effect sizes for per-stride parameters are in the range of small to medium (see Table 2), which again proves to be less effective than global parameters.

The total duration of turns showed a clear correlation with clinical scores, but such a correlation has not been obtained for per-stride timing. We may be able to explain this by considering two kinds of compensatory actions taken by patients in order to complete the turning. One compensatory action is to take smaller strides, and the other one is having longer pauses in a mid-stance phase in order to secure balance. While the first compensatory action decreases the per-stride time, the latter increases it. These compensatory actions may be different from patient to patient, and a patient may take both of these actions to safely complete a turn. Hence, overall, we cannot see any clear increase or decrease in the per-stride duration; however, the total turn duration did increase, because we may have a decrease of time per stride but patients take more strides that compensate for the decrease in time per stride. Having a long pause at mid-stance phases did not have any effect on stride and path length. These parameters decrease as the disease progresses.

Established clinical scores have no sub-item to assess specific characteristics of turning. Turning is evaluated as part of the gait in general; yet, our findings show that clinical scores reveal turning deficits at different levels. Parameters consistently show a higher correlation with gait and postural instability sub-items than with H&Y and UPDRS-III global scores, both in terms of p-values and effect sizes. Postural instability and gait sub-items are widely used for assessing gait, balance, and risk of falling in PD patients [23]. These two sub-items effectively demonstrate turning abnormalities, even at early stages of the disease (see Figure 2).

Despite the importance, there has not been a study to objectively compare straight walking and turning parameters in order to understand which set of parameters reflects gait abnormalities better. However, parameters quantifying straight walking differentiate between controls and PD patients in more moderate stages of the disease or higher levels of motor impairment [23]. Spatio-temporal parameters characterizing gait abnormalities have been widely used in data-driven applications, from PD diagnosis to disease monitoring [20,33]. However, most of such studies focus only on analyzing straight walking. Our results suggest that turning analysis may improve the performance of data-driven methods in medical applications.

One of the key goals of mobile gait analysis is to monitor patients outside of the clinics. Long-term monitoring of patients during the course of a day can provide better insight into their disease condition, in contrast to time-limited examinations inside the clinics [3]. Moreover, continuous monitoring of patients can be supplemented with preventative strategies for falling and FoG. The fact that turning during standardized tests demonstrates clear signs of deficiency emphasizes that turning analysis needs to be integrated into the long-term gait analysis. Turning isolation during long-term monitoring is even more challenging than in a standardized test, since the strides can be highly variable and different types of turning may happen within the course of a day. Some studies successfully addressed turning analysis in long-term monitoring [3,6], although they did not use on-shoe sensor systems. More research is needed to understand how findings of the current study can be transferred using an on-shoe sensor system to long-term monitoring.

Laterality of PD is another important factor in turning analysis, since turning to the direction of the most affected side is more challenging for patients. However, analyzing the laterality of the disease was beyond the scope of this study—here, the patients were instructed to turn at a convenient speed and preferred direction.

A limitation of our study is that we were, at this stage, not able to analyze the asymmetry between the left and right foot, since the sensors were not synchronized. Even better results may be obtained by an experiment design that takes into account the specific characteristics of PD patients and assessments during OFF medication.

#### **5. Conclusions**

Mobile gait analysis using wearable sensors offers elaborate assessments of pathological gait, leading to deeper insight into the motor deficits of PD. A high level of deficiency has been frequently reported for turning in PD. We investigated the feasibility of turning analysis during standardized gait tests using on-shoe wearable sensors. Turning measurements in our experiments clearly demonstrated turning deficits in Parkinson's patients. However, global parameters proved more effective than per-stride parameters. This should be taken into account in designing gait analysis systems, and has an important implication for PD clinical examinations, since physicians can readily assess global parameters. The current result is in alignment with other studies of turning in Parkinson's patients, which proves the feasibility of turning analysis using on-shoe sensor systems. The results of the current study can be applied to studies evaluating turning inside the clinic, and provide useful insight into long-term monitoring outside the clinic.

**Author Contributions:** Conceptualization, H.G., F.M., N.H.G., J.K. and B.M.E.; Formal analysis, N.H.G.; Funding acquisition, J.K. and B.M.E.; Methodology, H.G., N.H.G.; Resources, H.G. and J.K.; Software, J.H. and N.R., N.H.G.; Supervision, H.G., J.K. and B.M.E.; Writing—original draft, N.H.G.; Writing—review & editing, J.H., N.R., H.G., F.M., J.K. and B.M.E.

**Acknowledgments:** N. Haji Ghassemi acknowledges financial support from the Bavarian Research Foundation (BFS) and Federal Ministry of Education and Research (BMBF). This work was in part supported by the FAU Emerging Fields Initiative (EFIMoves), Bavarian Ministry for Economy, Regional Development & Energy via the Medical Valley Award 2017 (FallRiskPD Project) and EIT Health innovation project. F. Marxreiter is supported by the interdisciplinary center for clinical research Erlangen (IZKF), clinician scientist program. Björn M. Eskofier gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship program (grant number ES 434/8-1).

**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

#### **References**


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