**1. Introduction**

Aging is associated with numerous physiological problems that affect the brain. Some of these problems occur in the context of aging, such as cognitive deterioration and motor involvement, and often have an important impact on the central nervous system [1]. The causes of these deficits can be multifactorial and involve the central nervous system, the sensory receptors, the muscles, and the peripheral nerves [2]. On the other hand, there are comorbidities such as Parkinson's disease that can generate an even more marked deterioration of the motor skills of the affected elderly.

Parkinson's disease (PD) is a neurodegenerative disease that mainly affects people older than 60 years and is characterized by a neuronal loss in several areas and brain nuclei, but particularly in the substantia nigra, which can lead initially to motor alterations and delayed cognitive disorders that condition the patient to present physical dependence toward the caregiver and commitment to their autonomy [3].

Among the alterations mentioned are those associated with walking and arm swing. The march and its spatiotemporal characteristics have been analyzed since the Renaissance, and currently the analysis of this has become a very useful tool in the diagnostic evaluation and the severity of the disease, the response to treatment, as well as the impact of therapeutic interventions which can additionally predict the risk of falls [4]. Quantitative gait studies have usually focused on the characteristics of each participant and on the average of steps ignoring the step-by-step fluctuations between subjects. However, it has been shown for two decades that the magnitude of the step-by-step fluctuations and the changes over time during the march (gait dynamics) can be useful to understand the motor control of gait, in the quantification of the pathological and age-related alterations in the locomotion system and in an increase in objectivity in the measurement of mobility and functional status [5].

Motor alterations are one of the key points in the diagnosis of PD patients even in the early stages of the disease; however, the evaluation of the gait may be inconclusive because the slow and short steps are nonspecific and may be related to age, depressive disorders, and other conditions. On the other hand, we must remember that when patients meet the motor criteria for the diagnosis of PD, approximately 70% of the neurons of the substantia nigra have degenerated and the concentration of striatal dopamine has been reduced by 80% [6]; this shows that the typical motor manifestations of PE appear when there is already advanced neurodegeneration, and it has been determined that there is a "preclinical or prediagnostic phase" of PD [7].

Additionally, it is known that PD in early stages can start asymmetrically, since it can affect extremities of a hemibody predominantly and can even differentially affect the upper and lower limbs [8–10]. Thus, the asymmetry in the swing of the arms can be an opportunity for the earlier diagnosis of PE, even in the "prediagnosis stage" [10–14]. The function of swinging the arms during walking is to minimize the angular momentum of the body around the vertical axis [15]; however, there is still controversy as to whether it plays a role in gait stability. The coordination of the lower and upper limbs in the march is a complex phenomenon that has not yet been fully elucidated and involves circuits that we do not yet know. Previously it was thought that the movement of the arms was only passive (like a pendulum due to inertia) and did not imply muscle contraction [16]; however, Braune and Fischer when analyzing the march in a study postulated that this movement should present some muscle activity [17]. Much later, Ballesteros et al. were able to demonstrate with surface electromyography that there is an active muscle component involved which implies some control exerted by superior neural structures [18]. Another study showed that the amplitude of the swing of the arms is partly mediated by muscular activity, since, by reducing it, the amplitude of the swing of the arms decreases markedly by just depending on the passive component [15]. All of the above shows that the arm swing does depend in part on the CNS and can be measured, for example, in PD to observe alterations that correlate with the presence or absence of the disease.

Currently, the main reason for the disability in the world is adjudicated to neurological disorders; one of these is PD, which is the fastest growing, even faster than Alzheimer. In the last 25 years, the prevalence of PD is more than double, which generates double disability and deaths. The Global Burden of Disease study affirms that approximately 6.2 million people have PD. Currently, different subjective tools to assess and diagnose the PD are used in the clinical context; some of these tools are the DGI and UPDRS [19, 20].

However, with the rise of recent technologies, it has become possible to develop tools that allow taking objective measures to complement the diagnosis of Parkinson's; these measures focus on quantifying symptoms of the disease such as tremor, the amount and speed of the steps, as well as the amount of movement in the arms and their speed. However, these tools are considered difficult to access, according to their technological requirements, since they usually require up to 10 specialized cameras, a minimum space of 10 m2 , and must be handled by a clinical expert. Some of the assessment tools used in laboratory settings are motion-capture systems, such as GAITRite, Optitrack, Qualisys, and Vicon. These are used to obtain a quantitative and accurate gait representation, to help the analysis performed by the clinical expert in sport and physical rehabilitation and in gaming industries [20–22]. These systems are characterized by their high cost and complexity, since it requires

**5**

*Using Wavelets for Gait and Arm Swing Analysis DOI: http://dx.doi.org/10.5772/intechopen.84962*

tion and demands a long examination time.

disease, such as the asymmetry in rolling.

**2. Wavelet in biomedical applications**

allow voice control of a wheelchair [31].

through gait analysis [23–29].

a minimum technical expertise, enough space to capture test, and a patient prepara-

With the technological advance, different motion capture systems of medium complexity have been introduced to the market, able to generate clinically useful variables in medical environments, with a low cost and setup times. Microsoft Kinect was tested for use in the clinical context, as the primary motion-capture device; additionally, it has demonstrated sufficient accuracy for PD assessment

In addition to these devices, recent research has focused on efforts to build systems that support the clinical assessment from different perspectives; some of these are force platforms or pressure sensors, which are a set of sensors interconnected and located on the ground along a march corridor, and instrumented shoes, which include small force sensors placed on the template, which are used to detect the initial phases of the march, moments of festination, and freezing of the march. Other alternatives to tools based on RGBD cameras are those based on wearable devices such as accelerometers or inertial units. With these devices, solutions have been implemented to evaluate movement in the upper extremities and generate metrics to quantify the alterations. Ref. [30] is presented with a system for monitoring and measuring the swing of arms for patients. With Parkinson's disease, this system is composed of a set of handles with accelerometers, which allow extracting variables from the signals that may be related to the alterations generated by the

For decades, signal processing has been applied to multiple sectors such as industrial, military, health, and entertainment, among others. Regarding the health sector, these techniques have been used to facilitate access to technology and support or complement the diagnosis of a wide variety of diseases. As presented by Suk and Kojima, who use signal processing techniques to clean and extract information from speech signals to make speech recognition, with the purpose of generating a tool that allows disabled people to control by multiple voice home appliances and

Conventional processing techniques and methods allow to filter signals in a frequency range, extract relevant characteristics such as maximum and minimum peaks, fill data by interpolation, and transform and decompose signals in other domains such as frequency and time. Among these processing techniques, wavelet has shown to have a broad application panorama; the literature documents wavelet uses in different and varied fields such as detection of anomalies associated with seismic events in ultralow-frequency geomagnetic signals [32]; it is also possible to use wavelet techniques for image compression, as detailed in [33], who decompose into singular values and use a discrete wavelet transform to improve the maximum

In biomedical signals, wavelet transforms have also been suggested for signal compression [34], cardiac pattern recognition [35], EMG classification and decodification [34, 36], feature detection and extraction for ECGs [35] and PPGs [37], and epilepsy diagnosis [38]. Finally, in this chapter, we detail two potential usage scenarios for wavelet techniques, such as gait analysis and arm swing analysis. These two approaches were designed and tested in Parkinson's disease patients, but we consider are not limited to this population, other potential use cases are gait and arm swing alterations in stroke patients, gait analysis in patients with knee replace-

ratio of signal-to-noise ratio compared to techniques such as JPEG2000.

ment, and gait detection and recognition for surveillance.

*Using Wavelets for Gait and Arm Swing Analysis DOI: http://dx.doi.org/10.5772/intechopen.84962*

*Wavelet Transform and Complexity*

between subjects. However, it has been shown for two decades that the magnitude of the step-by-step fluctuations and the changes over time during the march (gait dynamics) can be useful to understand the motor control of gait, in the quantification of the pathological and age-related alterations in the locomotion system and in an increase in objectivity in the measurement of mobility and functional status [5]. Motor alterations are one of the key points in the diagnosis of PD patients even in the early stages of the disease; however, the evaluation of the gait may be inconclusive because the slow and short steps are nonspecific and may be related to age, depressive disorders, and other conditions. On the other hand, we must remember that when patients meet the motor criteria for the diagnosis of PD, approximately 70% of the neurons of the substantia nigra have degenerated and the concentration of striatal dopamine has been reduced by 80% [6]; this shows that the typical motor manifestations of PE appear when there is already advanced neurodegeneration, and it has been

determined that there is a "preclinical or prediagnostic phase" of PD [7].

alterations that correlate with the presence or absence of the disease.

are the DGI and UPDRS [19, 20].

specialized cameras, a minimum space of 10 m2

Currently, the main reason for the disability in the world is adjudicated to neurological disorders; one of these is PD, which is the fastest growing, even faster than Alzheimer. In the last 25 years, the prevalence of PD is more than double, which generates double disability and deaths. The Global Burden of Disease study affirms that approximately 6.2 million people have PD. Currently, different subjective tools to assess and diagnose the PD are used in the clinical context; some of these tools

, and must be handled by a clinical

However, with the rise of recent technologies, it has become possible to develop tools that allow taking objective measures to complement the diagnosis of Parkinson's; these measures focus on quantifying symptoms of the disease such as tremor, the amount and speed of the steps, as well as the amount of movement in the arms and their speed. However, these tools are considered difficult to access, according to their technological requirements, since they usually require up to 10

expert. Some of the assessment tools used in laboratory settings are motion-capture systems, such as GAITRite, Optitrack, Qualisys, and Vicon. These are used to obtain a quantitative and accurate gait representation, to help the analysis performed by the clinical expert in sport and physical rehabilitation and in gaming industries [20–22]. These systems are characterized by their high cost and complexity, since it requires

Additionally, it is known that PD in early stages can start asymmetrically, since it can affect extremities of a hemibody predominantly and can even differentially affect the upper and lower limbs [8–10]. Thus, the asymmetry in the swing of the arms can be an opportunity for the earlier diagnosis of PE, even in the "prediagnosis stage" [10–14]. The function of swinging the arms during walking is to minimize the angular momentum of the body around the vertical axis [15]; however, there is still controversy as to whether it plays a role in gait stability. The coordination of the lower and upper limbs in the march is a complex phenomenon that has not yet been fully elucidated and involves circuits that we do not yet know. Previously it was thought that the movement of the arms was only passive (like a pendulum due to inertia) and did not imply muscle contraction [16]; however, Braune and Fischer when analyzing the march in a study postulated that this movement should present some muscle activity [17]. Much later, Ballesteros et al. were able to demonstrate with surface electromyography that there is an active muscle component involved which implies some control exerted by superior neural structures [18]. Another study showed that the amplitude of the swing of the arms is partly mediated by muscular activity, since, by reducing it, the amplitude of the swing of the arms decreases markedly by just depending on the passive component [15]. All of the above shows that the arm swing does depend in part on the CNS and can be measured, for example, in PD to observe

**4**

a minimum technical expertise, enough space to capture test, and a patient preparation and demands a long examination time.

With the technological advance, different motion capture systems of medium complexity have been introduced to the market, able to generate clinically useful variables in medical environments, with a low cost and setup times. Microsoft Kinect was tested for use in the clinical context, as the primary motion-capture device; additionally, it has demonstrated sufficient accuracy for PD assessment through gait analysis [23–29].

In addition to these devices, recent research has focused on efforts to build systems that support the clinical assessment from different perspectives; some of these are force platforms or pressure sensors, which are a set of sensors interconnected and located on the ground along a march corridor, and instrumented shoes, which include small force sensors placed on the template, which are used to detect the initial phases of the march, moments of festination, and freezing of the march.

Other alternatives to tools based on RGBD cameras are those based on wearable devices such as accelerometers or inertial units. With these devices, solutions have been implemented to evaluate movement in the upper extremities and generate metrics to quantify the alterations. Ref. [30] is presented with a system for monitoring and measuring the swing of arms for patients. With Parkinson's disease, this system is composed of a set of handles with accelerometers, which allow extracting variables from the signals that may be related to the alterations generated by the disease, such as the asymmetry in rolling.
