Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1–4 were assessed with two gait tasks—20 m straight walk and circular walk—using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD.

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

Gait and balance dysfunctions are common in neurological disorders and have a negative effect on quality of life. Regularly quantifying these mobility limitations can be used to measure disease progression and the effect of treatment. This information can be used to provide a more individualized treatment. Inertial measurement units (IMUs) can be utilized to quantify mobility in different contexts. However, algorithms are required to extract valuable parameters out of the raw IMU data. These algorithms need to be validated to make sure that they extract the features they should extract. This validation should be performed per disease since different mobility limitations or symptoms can influence the performance of an algorithm in different ways. Therefore, this dataset contains data from both healthy subjects and patients with neurological diseases (Parkinson’s disease, stroke, multiple sclerosis, chronic low back pain). The full bodies of 167 subjects were measured with IMUs and an optical motion capture (reference) system. Subjects performed multiple standardized mobility assessments and non-standardized activities of daily living. The data of 21 healthy subjects are shared online, data of the other subjects and patients can only be obtained after contacting the corresponding author and signing a data sharing agreement.

Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12–24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters’ measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39–57%, followed by acceleration in the ML (43–55%) and AP direction (54–77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters.

Evaluating gait is part of every neurological movement disorder assessment. Generally, the physician assesses the patient based on their experience, but nowadays inertial measurement units (IMUs) are also often integrated in the assessment. Instrumented gait analysis has a longstanding tradition and temporal parameters are used to compare patient groups or trace disease progression over time. However, the day-to-day variability needs to be considered especially in specific patient cohorts. The aim of the study was to examine day-to-day variability of temporal gait parameters of two experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. We recruited 49 participants (24 women (age: 78 years ± 6 years, BMI = 25.1 kg/m² and 25 men (age: 77 years ± 6 years, BMI = 26.5 kg/m²)) from the neurogeriatric ward. Two gait distances (4 m and 20 m) were performed during the first session and repeated the following day. To evaluate reliability, the Intraclass Correlation Coefficient (ICC2,k) and minimal detectable change (MDC) were calculated for the number of steps, step time, stride time, stance time, swing time, double limb support time, double limb support time variability, stride time variability and stride time asymmetry. The temporal gait parameters showed poor to moderate reliability with mean ICC and mean MDC95% values of 0.57 ± 0.18 and 52% ± 53%, respectively. Overall, only four out of the nine computed temporal gait parameters showed high relative reliability and good absolute reliability values. The reliability increased with walking distance. When only investigating steady-state walking during the 20 m walking condition, the relative and absolute reliability improved again. The most reliable parameters were swing time, stride time, step time and stance time. Study results demonstrate that reliability is an important factor to consider when working with IMU derived gait parameters in specific patient cohorts. This advocates for a careful parameter selection as not all parameters seem to be suitable when assessing gait in neurogeriatric patients.

Essential tremor (ET) is a progressive movement disorder whose pathophysiology is not fully understood. Current evidence supports the view that the cerebellum is critically involved in the genesis of the tremor in ET. However, it is still unknown whether cerebellar dysfunction affects not only the control of current movements but also the prediction of future movements through dynamic adaptation toward a changed environment. Here, we tested the capacity of 28 patients with ET to adapt in a visuomotor adaptation task known to depend on intact cerebellar function. We found specific impairments in that task compared to age-matched healthy controls. Adaptation to the visual perturbation was disrupted in ET patients, while de-adaptation, the phase after abrupt removal of the perturbation, developed similarly to control subjects. Baseline tremor-independent motor performance was as well similar to healthy controls, indicating that adaptation deficits in ET patients were not rooted in an inability to perform goal-directed movements. There was no association between clinical severity scores of ET and early visuomotor adaptation abilities. These results provide further evidence that the cerebellum is dysfunctional in ET.