Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

Sigma-Lognormal Modeling of Speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject’s age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics-based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma-lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR-TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma-Lognormal Model: A Proof of Concept

Background

Clinical tests for detecting central and peripheral shoulder fatigue are limited. The discrimination of these two types of fatigue is necessary to better adapt recovery intervention. The Kinematic Theory of Rapid Human Movements describes the neuromotor impulse response using lognormal functions and has many applications in pathology detection. The ideal motor control is modeled and a change in the neuromuscular system is reflected in parameters extracted according to this theory.

Objective

The objective of this study was to assess whether a shoulder neuromuscular fatigue could be detected through parameters describing the theory, if there is the possibility to discriminate central from peripheral fatigue, and which handwriting test gives the most relevant information on fatigue.

Methods

Twenty healthy participants performed two sessions of fast stroke handwriting on a tablet, before and after a shoulder fatigue. The fatigue was in internal rotation for one session and in external rotation during the other session. The drawings consisted of simple strokes, triangles, horizontal, and vertical oscillations. Parameters of these strokes were extracted according to the Sigma–Lognormal model of the Kinematic Theory. The evolution of each participant was analyzed through a U-Mann–Whitney test for individual comparisons. A Hotelling’s T²-test and a U-Mann–Whitney test were also performed on all participants to assess the group evolution after fatigue. Moreover, a correlation among parameters was calculated through Spearman coefficients to assess intrinsic parameters properties of each handwriting test.

Results

Central and peripheral parameters were statistically different before and after fatigue with a possibility to discriminate them. Participants had various responses to fatigue. However, when considering the group, parameters related to the motor program execution showed significant increase in the handwriting tests after shoulder fatigue. The test of simple strokes permits to know more specifically where the fatigue comes from, whereas the oscillations tests were the most sensitive to fatigue.

Conclusion

The results of this study suggest that the Sigma–Lognormal model of the Kinematic Theory is an innovative approach for fatigue detection with discrimination between the central and peripheral systems. Overall, there is a possibility to implement the setting for clinics and sports personalized follow-up.

iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.

Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test

Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub–tasks with a single IMU automatically providing information about variables such as: duration of sub–tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting.

The transition between turning and sitting in patients with Parkinson's disease: A wearable device detects an unexpected sequence of events

BACKGROUND

When older adults turn to sit, about 80% of the subjects complete the turn before starting to sit i.e., a distinct-strategy, while in about 20%, part of the turning and sitting take place concurrently, i.e., an overlapping-strategy. A prolonged duration of the separation between tasks in the distinct-strategy (D-interval) and a prolonged duration of the overlap interval in overlapping-strategy (O-interval) are related to worse motor symptoms and poorer cognition. In the present study, we evaluated what strategy is employed by patients with Parkinson's disease (PD) when they transition from turning to sitting.

METHODS

96 participants with PD performed turn to sit as part of the Timed Up and Go test, both with and without medications, while wearing a body-fixed sensor. We quantified the turn-to-sit transition and determined which strategy (distinct or overlapping) was employed. We then stratified the cases and used regression models adjusted for age, gender, height, and weight to examine the associations of the D-interval or O-interval with parkinsonian features and cognition.

RESULTS

Most patients (66%) employed the overlapping-strategy, both off and on anti-parkinsonian medications. Longer O-intervals were associated with longer duration of PD, more severe PD motor symptoms, a higher postural-instability-gait-disturbance (PIGD) score, and worse freezing of gait. Longer D-intervals were not associated with disease duration or PD motor symptoms. Neither the D- nor O-intervals were related to cognitive function. Individuals who employed the overlapping-strategy had more severe postural instability (i.e., higher PIGD scores), as compared to those who used the distinct-strategy.

SIGNIFICANCE

In contrast to older adults without PD, most patients with PD utilize the overlapping strategy. Poorer postural and gait control are associated with the strategy choice and with the duration of concurrent performance of turning and sitting. Additional work is needed to further explicate the mechanisms underlying these strategies and their clinical implications.

Hemodialysis Impact on Motor Function beyond Aging and Diabetes-Objectively Assessing Gait and Balance by Wearable Technology

Motor functions are deteriorated by aging. Some conditions may magnify this deterioration. This study examined whether hemodialysis (HD) process would negatively impact gait and balance beyond diabetes condition among mid-age adults (48–64 years) and older adults (65+ years). One hundred and ninety-six subjects (age = 66.2 ± 9.1 years, body-mass-index = 30.1 ± 6.4 kg/m², female = 56%) in 5 groups were recruited: mid-age adults with diabetes undergoing HD (Mid-age HD+, n = 38) and without HD (Mid-age HD−, n = 40); older adults with diabetes undergoing HD (Older HD+, n = 36) and without HD (Older HD−, n = 37); and non-diabetic older adults (Older DM−, n = 45). Gait parameters (stride velocity, stride length, gait cycle time, and double support) and balance parameters (ankle, hip, and center of mass sways) were quantified using validated wearable platforms. Groups with diabetes had overall poorer gait and balance compared to the non-diabetic group (p < 0.050). Among people with diabetes, HD+ had significantly worsened gait and balance when comparing to HD− (Cohen’s effect size d = 0.63–2.32, p < 0.050). Between-group difference was more pronounced among older adults with the largest effect size observed for stride length (d = 2.32, p < 0.001). Results suggested that deterioration in normalized gait speed among HD+ was negatively correlated with age (r = −0.404, p < 0.001), while this correlation was diminished among HD−. Interestingly, results also suggested that poor gait among Older HD− is related to poor ankle stability, while no correlation was observed between poor ankle stability and poor gait among Older HD+. Using objective assessments, results confirmed that the presence of diabetes can deteriorate gait and balance, and this deterioration can be magnified by HD process. Among HD− people with diabetes, poor ankle stability described poor gait. However, among people with diabetes undergoing HD, age was a dominate factor describing poor gait irrespective of static balance. Results also suggested feasibility of using wearable platforms to quantify motor performance during routine dialysis clinic visit. These objective assessments may assist in identifying early deterioration in motor function, which in turn may promote timely intervention.

Cranio-Caudal Kinematic Turn Signature Assessed with Inertial Systems As a Marker of Mobility Deficits in Parkinson's Disease

Background

Turning is a challenging mobility task requiring proper planning, coordination, and postural stability to be executed efficiently. Turn deficits can impair mobility and lead to falls in patients with neurodegenerative disease, such as Parkinson's disease (PD). It was previously shown that the cranio-caudal sequence involved during a turn (i.e., motion is initiated by the head, followed by the trunk) exhibits a signature that can be captured using an inertial system and analyzed through the Kinematics Theory. The so-called cranio-caudal kinematic turn signature (CCKS) metrics derived from this approach could, therefore, be a promising avenue to develop and track markers to measure early mobility deficits.

Objective

The current study aims at exploring the discriminative validity and sensitivity of CCKS metrics extracted during turning tasks performed by patients with PD.

Methods

Thirty-one participants (16 asymptomatic older adults (OA): mean age = 69.1 ± 7.5 years old; 15 OA diagnosed with early PD ON and OFF medication, mean age = 65.8 ± 8.4 years old) performed repeated timed up-and-go (TUG) tasks while wearing a portable inertial system. CCKS metrics (maximum head to trunk angle reached and commanded amplitudes of the head to trunk neuromuscular system, estimated from a sigma-lognormal model) were extracted from kinematic data recorded during the turn phase of the TUG tasks. For comparison purposes, common metrics used to analyze the quality of a turn using inertial systems were also calculated over the same trials (i.e., the number of steps required to complete the turn and the turn mean and maximum velocities).

Results

All CCKS metrics discriminated between OA and patients (p ≤ 0.041) and were sensitive to change in PD medication state (p ≤ 0.033). Common metrics were also able to discriminate between OA and patients (p < 0.014), but they were unable to capture the change in medication state this early in the disease (p ≥ 0.173).

Conclusion

The enhanced sensitivity to change of the proposed CCKS metrics suggests a potential use of these metrics for mobility impairments identification and fluctuation assessment, even in the early stages of the disease.