Biomechanical loading as an alternative treatment for tremor: a review of two approaches

Background

Tremor is the most common movement disorder and strongly increases in incidence and prevalence with aging. Although not life threatening, upper-limb tremors hamper the independence of 65% of people suffering from them affected persons, greatly impacting their quality of life. Current treatments include pharmacotherapy and surgery (thalamotomy and deep brain stimulation). However, these options are not sufficient for approximately 25% of patients. Therefore, further research and new therapeutic options are required to effectively manage pathological tremor.

Methods

This paper presents findings of two research projects in which two different wearable robots for tremor management were developed based on force loading and validated. The first consisted of a robotic exoskeleton that applied forces to tremulous limbs and consistently attenuated mild and severe tremors. The second was a neuroprosthesis based on transcutaneous neurostimulation. A total of 22 patients suffering from parkinsonian or essential tremor (ET) of different severities were recruited for experimental validation, and both systems were evaluated using standard tasks employed for neurological examination. The inclusion criterion was a postural and/or kinetic pathological upper-limb tremor resistant to medication.

Results

The results demonstrate that both approaches effectively suppressed tremor in most patients, although further research is required. The work presented here is based on clinical evidence from a small number of patients (n = 10 for robotic exoskeleton and n = 12 for the neuroprosthesis), but most had a positive response to the approaches. In summary, biomechanical loading is non-invasive and painless. It may be effective in patients who are insufficiently responsive (or have adverse reactions) to drugs or in whom surgery is contraindicated.

Discussion

This paper identifies and evaluates biomechanical loading approaches to tremor management and discusses their potential.

Characterizing head motor disorders to create novel interfaces for people with cerebral palsy: creating an alternative communication channel by head motion

This paper aims to validate a head mounted inertial interface to characterize disorder movements in people with cerebral palsy (CP). The kinematic patterns extracted from this study will be used to design an alternative communication channel (using head motion) adapted to user's capabilities and limitations. Four people with CP participated (GMFCS level V) and three healthy subjects as reference group. The main outcome measures were divided into 1) Time-domain, 2) Frequency-domain and 3) Spatial domain. Results showed that the inertial interface succeeds assessing the pathological motion. Firstly, the system differentiates between voluntary and involuntary motion in terms of motor control, frequency and range of motion. Secondly some motion disorders such as hypertonia, hypotonia can be identified. These results suggest that people with motor disorders could benefit from the developed inertial system in three fields: 1) diagnosis of motor disorder by means of an objective quantification, 2) physical and cognitive rehabilitation by means of proprioceptive enhancement through visual-motor feedback and 3) functional compensation by means of an inertial person-machine interface for controlling computer and assistive devices (e.g. wheelchairs or walkers).

A modelling study on transmission of the central oscillator in tremor by a motor neuron pool

In spite of decades of intense research, pathological tremors still constitute unknown disorders. This study addresses, based on a multi-scale model, the behavior of an entire pool of motor neurons in tremor, under the hypothesis that tremor is an oscillation of central origin commonly projected to all motor neurons that innervate a muscle. Our results show that under such conditions both paired discharges and enhanced motor neuron synchronization, two of the characteristic landmarks of tremor, emerge. Moreover, coherence and correlation analyses suggest that the central tremor oscillator is transmitted linearly by the motor neuron pool given that a small set (7 or 8) of motor neurons are sampled.

Multimodal BCI-mediated FES suppression of pathological tremor

Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.

Empowering and Assisting Natural Human Mobility: The Simbiosis Walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staff.

Extraction of user's navigation commands from upper body force interaction in walker assisted gait

Background

The advances in technology make possible the incorporation of sensors and actuators in rollators, building safer robots and extending the use of walkers to a more diverse population. This paper presents a new method for the extraction of navigation related components from upper-body force interaction data in walker assisted gait. A filtering architecture is designed to cancel: (i) the high-frequency noise caused by vibrations on the walker's structure due to irregularities on the terrain or walker's wheels and (ii) the cadence related force components caused by user's trunk oscillations during gait. As a result, a third component related to user's navigation commands is distinguished.

Results

For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error ((2.035 ± 0.358)·10^-2 kgf) and delay ((1.897 ± 0.3697)·10¹ms). A Fourier Linear Combiner filtering architecture was implemented for the adaptive attenuation of about 80% of the cadence related components' energy from force data. This was done without compromising the information contained in the frequencies close to such notch filters.

Conclusions

The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.

Application of EMD as a novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency-energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

A new platform based on IEEE802.15.4 wireless inertial sensors for motion caption and assessment

Systems for motion caption and assessment in biomechanics are mostly based on photogrammetry. These systems are restricted to the movement analysis lab and moreover, they are very expensive. New advances in MEMs (Microelectromechanical) and wireless technologies enable inertial sensing as an alternatives for motion caption. This paper presents a wireless inertial sensor including 3 linear accelerometers, 3 gyroscopes and 3 magnetometers. The IMU (inertial measurement unit) includes a IEEE802.15.4 compliant transceiver. The platform expands the frontiers of movement analysis for motion caption in real scenarios like sports and wearable robotics since it does not need structurated labs. Besides the advantages, the cost of the platform is much lower comparing actual photogrammetry systems.

Design and validation of a rehabilitation robotic exoskeleton for tremor assessment and suppression

Exoskeletons are mechatronic systems worn by a person in such a way that the physical interface permits a direct transfer of mechanical power and exchange of information. Upper limb robotic exoskeletons may be helpful for people with disabilities and/or limb weakness or injury. Tremor is the most common movement disorder in neurological practice. In addition to medication, rehabilitation programs, and deep brain stimulation, biomechanical loading has appeared as a potential tremor suppression alternative. This paper introduces the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression) that provides a means of testing and validating nongrounded control strategies for orthotic tremor suppression. This paper describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components. Two control strategies developed for tremor suppression with exoskeletons are described. These two strategies are based on biomechanical loading and notch filtering the tremor through the application of internal forces. Results from experiments using these two strategies on patients with tremor are summarized. Finally, results from clinical trials are presented, which indicate the feasibility of ambulatory mechanical suppression of tremor.

Mechanical suppression of essential tremor

This paper describes a new treatment for essential tremor. A wearable orthosis, which can be adapted to each configuration of each joint of the upper limb, is able to apply effective dynamic force between consecutive segments of the upper limb and change its biomechanical characteristics. The orthosis is controlled by a computer with a dedicated software application that distinguishes between real time tremor and voluntary movement. The wearable orthosis is able to detect position, rate and acceleration of rotation of the joint by means of a chip gyroscope. This technology was evaluated in six patients suffering from essential tremor. The technique is non invasive and represents an alternative to medication and deep brain stimulation.

Evaluation of a wearable orthosis and an associated algorithm for tremor suppression

We describe a wearable orthosis and an associated algorithm for the simultaneous assessment and treatment of essential tremor, one of the most common movement disorders in humans involving an overactivity of the olivo-cerebellar pathways. A motor providing effective viscosity is fixed on a wearable orthosis in the upper limbs. The motor is controlled by a personal computer with software processing in real time the position and rate of rotation of the joint detected by a chip gyroscope. The orthosis can be used in a monitoring mode and in an active mode. The range of tremor suppression of the signals above the orthosis operational limit ranges from about 3% (percentile 5) to about 79% (percentile 95) in relation to energy in the monitoring mode. Considering both postural and kinetic, the mean tremor energy decreased from 55.49 +/- 22.93 rad2 s(-3) in the monitoring mode to 15.66 +/- 7.29 rad2 s(-3) in the active mode. Medians of power reduction were below 60% for the wrist and the elbow. In addition to supplying new information on the interactions between kinematics, dynamics and tremor genesis, this non-invasive technique is an alternative to current therapies. This new approach will provide new insights into the understanding of motor control.

The tremor coherence analyzer (TCA): a portable tool to assess instantaneous inter-muscle coupling in tremor

This paper proposes a novel system for pathological tremor study and diagnosis. The system described called TCA (tremor coherence analyzer) is based on a electronic device developed for wireless monitoring of physiological variables. The device uses Bluetooth technology to communicate. The proposed technique for pathological tremor analysis uses surface EMG signals. The EMG sensors are located on forearm muscles to measure muscular activity due to pathological tremor. The coherence function between these signals is calculated. The application of the coherence function allows to determine linear dependencies between two signals.