AI-driven Sensor Technologies in Physiotherapy and Oncology: Transforming Rehabilitation Through Intelligent Biomechanical Monitoring


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AI-driven Sensor Technologies in Physiotherapy and Oncology: Transforming Rehabilitation Through Intelligent Biomechanical Monitoring



The integration of biomedical sensors into physiotherapy has revolutionized the assessment and rehabilitation of human movement. These sensors, both invasive and noninvasive, measure critical biomechanical parameters such as force, pressure and motion, enabling physiotherapists to conduct accurate diagnostics and monitor rehabilitation progress. Traditional silicon-based sensors such as accelerometers, gyroscopes and magnetometers form the backbone of wearable technologies, enabling real-time tracking of body movement. Complementing these are flexible sensors, including textile-based strain and pressure sensors, offering superior comfort and adaptability for patients.


The chapter also explores biophysical measurements, emphasizing the role of force and pressure sensors in evaluating muscle strength, joint stability and gait dynamics. Modern physiotherapy uses advanced tools such as digital goniometers, tilt sensors and inertial measurement units (IMUs) for comprehensive joint mobility assessments. Additionally, artificial intelligence (AI) is being integrated with sensor technologies to enhance rehabilitation outcomes. AI enables predictive analysis, personalized treatment planning and real-time biofeedback, thereby improving patient motivation and safety. The synergy between AI and sensor data facilitates early detection of abnormalities, risk prevention and continuous remote monitoring. Together, these innovations ensure precision-driven, patient-centered physiotherapy interventions.


This chapter underscores the importance of ongoing research and technological advancement in developing smarter, safer and more efficient rehabilitation tools tailored to individual patient needs.


11.1. Introduction


Physiotherapy research frequently uses biomedical sensors to measure a variety of biomechanical factors. Often, they are called biomedical transducers and are the main building blocks of diagnostic medical instrumentation found in physiotherapists’ and biomedical engineers’ laboratories (Jones 2010).


The need for accurate medical diagnostic procedures places stringent requirements on the design and use of biomedical sensors (Jones 2010). Usually, the first step in developing a biomedical sensor is to access the accuracy, sensor operating range, response time, sensitivity, resolution and reproducibility of the sensor. Once the need of the device is met, it becomes easy to choose among various available sensors and ensure that the measurement remains sensitive, stable, safe and cost-effective (Enderle and Bronzino 2012).


Sensors also provide a non-hindrance assessment of human kinematic motion and motion status during exercise training or during rehabilitation. Nevertheless, there is a significant lack of studies on motion monitoring index systems, comprising rigid and/or flexible sensors, with a well-designed wearable system along with different angles of joint motion, especially for gross trunk mobility, complex joint movements and limb movements (Jones 2010; Enderle and Bronzino 2012).


11.1.1. Sensor classification


Depending on their particular applications, sensors are usually categorized as physical, electrical or chemical based on the quantity to be measured (Jones 2010). Biosensors are a special subclassification of sensors that have two distinct components:



  1. a biomechanical recognition element that serves as a mediator and offers the selectivity required to detect the mechanical component of interest, such as movement, range of motion, depth or muscle strength;
  2. a supporting structure that is in close proximity to the biomechanical component and serves as a transducer.

The transducer’s function is to convert the mechanical reaction into visual, electrical or physical signals proportional to the concentration of particular biomechanics (Enderle and Bronzino 2012).


Both invasive and noninvasive sensors used by biomedical engineers will be covered in this chapter.


Physiotherapy and rehabilitation can make use of a variety of mechanical sensors. Every sensor serves a distinct purpose, either supporting or carrying out the biomechanical processes of the human body. Physiotherapy equipment, investigative or therapy related, is mostly noninvasive with the sensor being attached to the external body part.


Thus, sensors can be classified into several categories (Bowman and Meindl 1986; Mendelson 2005; Jones 2010; Enderle and Bronzino 2012):



  1. On the basis of invasiveness:

    1. invasive sensors, which are inside the body;
    2. noninvasive sensors, which are outside the body.

  2. On the basis of function:

    1. active sensors require an external power source to function, for example, accelerometers, pressure sensors, light sensors, temperature sensors and sound sensors;
    2. passive sensors are known to usually generate their own electric signal to function. Examples include vibrators, electric field sensors and infrared sensors;
    3. analog sensors produce a continuous output signal or measurement;
    4. digital sensors show the reading digitally on the device itself.

11.1.2. Sensor packaging


Whenever the sensor comes into contact with body fluids, the host itself may affect the sensor’s function, or the sensor may affect the site in which it is implanted. Hence, the materials used in the construction of the sensor’s outer body must be non-thrombogenic and nontoxic since they play a critical role in determining the overall performance and longevity of an implantable sensor (Bowman and Meindl 1986; Sani et al. 2021).


11.2. Sensors for monitoring human motion


According to Huang et al. (2023) motion monitoring sensors can be classified on the basis of:



  1. a human motion gesture monitoring index system;
  2. sensor characteristics;
  3. system design.

Physiological signals, such as electromyography (EMG) and electrocardiography (ECG), can also identify muscle contractions and heart contractions, the latter of which is a muscle. They do, however, function as a less accurate indirect method for gross motor training, particularly when it comes to electromyography. Recent developments in EMG have suggested the use of needle electrodes, which provide a detailed analysis of the fiber recruitment method. However, it becomes challenging to use the voltage and frequency patterns of muscle activity to determine the precise motion amplitude or angle, particularly in a 3D gross motor assessment.


11.2.1. Traditional silicon-based sensors



  • Accelerometers: devices that can measure the acceleration of the part to which it is attached. According to an accelerometer’s measurement principle, there are two types:

    1. linear accelerometers, which detect linear acceleration;
    2. angular accelerometers, which detect angular acceleration.

Accelerometers use their electrical impulses to identify deflection or stress variances from external acceleration. Gait monitoring, human motion analysis, fall detection and energy consumption estimation are all common uses for these. Three-axial accelerometers, which have an accuracy of ± 2 g and a sampling rate of 50–100 Hz, are currently commonly used. These days, most wearable systems are made up of triaxial accelerometers, which are frequently positioned on the body’s moving parts, such as joints and limbs (Huang et al. 2023).

A diagram illustrates human body movements and various joints, including head, trunk, upper and lower limbs, and specific joints like knee, hip, and elbow.

Figure 11.1. Diagram of an accelerometer showing its components and basic working principle

A photograph of a black accelerometer sensor with a cable, labeled M E A S. A photograph of a black sensor chip labeled Colibrys, MS9005 D C504 005. Three photos including a person wearing a black vest with multiple sensors attached, a close up of a sensor, and a circuit board detailing accelerometer, gyroscope, and magnetometer components.

Figure 11.2. Two basic types of accelerometers commonly used in prototype builds


The above images are two basic types of accelerometers commonly used in prototype builds (Niu et al. 2018).



  • Gyroscope: a sensor that uses the Coriolis force to measure the angular variance or angular rate. Accelerometers and gyroscopes are frequently used in tandem to detect the angular velocity of human motion in order to reduce error and increase monitoring accuracy. There are many gyroscopes available on the market, for example, L3G4200D, IMUZ, 3DM-GX3-25 and ISM330DHCX, which are multiaxial with the minimum axis being 3. The application area of what they are validated for differs variably from forearm to upper arms, waist and trunk (Schiefer et al. 2011).
A set of 7 photographs of a person wearing a gyroscope sensor on their legs and waist, demonstrating its use in measuring angular variance during motion.

Figure 11.3. Example of a gyroscope sensor for measuring angular variance in human motion



  • Magnetometer: this uses the Earth’s magnetic field to detect motion. Static motion yields maximum accuracy and determines the wearer’s direction by measuring the flux of the local magnetic field. In order to rectify the heading angle offset brought about by gyroscope drift, accelerometers and gyroscopes are frequently combined to create an inertial measurement unit, or IMU (Huang et al. 2023; Zhang et al. 2023).
A schematic diagram illustrates a magnetometer detecting orientation using Earth's magnetic field with inertial sensors.

Figure 11.4. Schematic of a magnetometer used for detecting orientation based on Earth’s magnetic field



  • Inertial sensors: accelerometers, gyroscopes and magnetometers form an inertial measuring unit called an IMU. IMUs ensure accurate monitoring measurements, especially movements with three or more axes. The most popular inertial sensors for wearable technologies are made by InvenSense, Bosch Sensortec and Shimmer in Germany, and they are typically nine-axis (Ahmadi et al. 2015; Huang et al. 2023).
A diagram of an inertial measurement unit with accelerometer, gyroscope, and magnetometer sensors positioned on a leg.

Figure 11.5. An inertial measurement unit (IMU) combining accelerometer, gyroscope and magnetometer sensors



  • Tilt sensors: these sensors use a combination of gyroscopes and accelerometers and are based on the gravitational acceleration theory. The wearer’s acceleration can be monitored using three-axial systems (x, y and z), and the angle between each axis and the corresponding acceleration of gravity can then be calculated. These are frequently used to identify postural changes and are also referred to as inclination sensors (Huang et al. 2023).

11.2.2. Flexible sensors


Excellent stretchability, biocompatibility, good compliance and high sensitivity are all included in them. The flexible sensor is used for augmented and virtual reality, human–computer interaction and human mobility monitoring and can be separated into two main groups: thin-film sensors and textile sensors, both of which have pressure and strain sensors (Huang et al. 2023).



  • Substrates of flexible sensors: these textile sensors are composed of a variety of materials, including fabrics, yarns and fibers. In order to detect materials and maintain posture, the textile sensor is typically composed of materials that are omnidirectional, extremely elastic, permeable, hydrophobic, long-lasting, biocompatible and lightweight. At first, textiles were only used as carriers, with the rigid parts of electrical gadgets incorporated in or attached to them. Later, rigid microelectronic components combined with flexible fabric devices gained popularity, creating a heterogeneous technology that helped create flexible electronic components. These days, this hybrid technology is widely used to measure human motion. Figure 11.6 shows such an image representing the sensors used on a daily basis (Huang et al. 2023).
A multi-part figure illustrates flexible textile-based sensors integrated into various daily-use garments.

Figure 11.6. Example of flexible textile-based sensors integrated into daily-use garments (Huang et al. 2023)



  • Flexible strain and pressure sensors: strain sensors translate a mechanical motion’s degree of deformation – the alteration in the structure’s shape upon the application of a load – into electrical signals. There are three categories in which these fall:

    1. resistive;
    2. capacitive;
    3. voltage-based.

Current research suggests that strain sensors are commonly used to detect changes in the body’s position and for kinesthetic monitoring (Huang et al. 2023).


Pressure sensors translate the pressure into an electrical signal, in contrast to strain sensors. They can be used extensively to detect finger motion, including pressure, twisting, extension and flexion force, and they have a detecting capacity of above 10 kPa. Joint motion recognition is another application for these sensors. These are now used for plantar pressure distribution during stance and gait monitoring. They are further illustrated in Figures 11.7 and 11.8 (Huang et al. 2023).

A multi-part diagram illustrates flexible strain sensors translating mechanical deformation into electrical signals.

Figure 11.7. Diagram of a flexible strain sensor translating mechanical deformation into electrical signals

A multi-part figure illustrates a flexible pressure sensor with various components for detecting force and pressure distribution.

Figure 11.8. Diagram of a flexible pressure sensor for detecting applied force and pressure distribution


11.3. Biophysical measurements in physiotherapy


These measurements study specific functions which change over time. These changes can yield significant statistical data, which can be used to determine the effectiveness of the therapy or assist in diagnosis.


Biophysics is defined as “the physical and mechanical properties of the cells/tissues which can be used to either assess or modify functioning of the cells/tissues and can be studied in detail” (Jones 2010).


Some examples of biophysical measurements in physiotherapy include:



  • Electrotherapy: these form a major part of adjunct therapy, where the electrotherapeutic modalities are used to treat pain, reduce swelling, inflammation and post-therapy soreness, assess the physiological characteristics of nerves by nerve conduction velocity, faradic curve and various other methods. The modalities preferred in electrotherapy are majorly noninvasive. The basic mechanism of electricity conduction in these modalities are listed in Table 11.1 (Bracciano 2024).

Table 11.1. Components of electrotherapy modalities and their functions in physiotherapy



















Parts Function
Modality Generates the current in required frequency.
Cables Pass the current from the modality/system to the electrode (the contacting part of the machine to the body).
Electrodes These are commonly made up of rubber or metal in order to conduct the electric current from the machine to the part in contact.
Aqua Gel Decreases the resistance between the electrode and the skin so that the current passes smoothly.


  • Physical therapists: these can use electrical currents to stimulate muscles, which can help to reduce swelling, improve movement and maintain muscle tone and strength.
  • Ultra short-wave therapy: research in this area has improved understanding of how high frequency energy is distributed and converted in the body. This research has led to the investigation of external conditions, such as the size, shape and arrangement of electrodes, as well as the distribution of energy within the body (Nazar and Tahir 2020).

Noninvasive sensors are most commonly used in physiotherapy. The biopotential measurements required in physiotherapy are as follows: force measurement (load or weight); muscular force measurement; joint movement measurement; fascia pliability measurement; response or reaction time measurement.


A few sensors along with their biomechanical measurements and uses are mentioned below. Yet, there are many biomechanical functions which can be developed or are under-development, necessitating further studies.


11.3.1. Force measurement


Force measurement is crucial for physiotherapists as it provides objective data to assess muscle strength, joint function and biomechanical performance during rehabilitation. By measuring the force exerted during exercises, physiotherapists can track a patient’s progress, ensure safe load-bearing and adjust rehabilitation plans accordingly. Force measurement helps prevent re-injury by avoiding overloading or underloading the affected area, guiding exercise progression and ensuring the rehabilitation process is tailored to the patient’s specific needs, promoting optimal recovery and functional outcomes.


Force measurement during gait is crucial in physiotherapy as it helps assess how weight is distributed across the joints and muscles while walking. This data enables physiotherapists to identify movement abnormalities, such as uneven load-bearing or muscle weakness, and tailor rehabilitation programs to address these issues. By monitoring gait forces, therapists can ensure more effective recovery and prevent re-injury, promoting better mobility and overall function.


Biomechanically, gait is classified into two major phases: swing and stance phases.


The stance phase is where the lower extremity touches the ground and the swing phase is where the lower extremity is off the ground. The main biomechanical analysis of gait in either phase is:



  • Stance phase: area of weight bearing of the foot and angular motions of hip-knee and angle joints along with spine and upper extremities, in addition to muscle recruitment strategy for stability.
  • Swing phase: angular motions of hip-knee and ankle joints along with spine and upper extremities, in addition to muscle recruitment strategy for mobility (Guerra et al. 2020).

Thus, as stated, the area of weight bearing is given more importance in stance phase than swing. The quantitative measurement of the amount of force induced by the muscle during stance and swing phases varies as the movements of the joint differ. Thus, quantitative assessment becomes equally important along with qualitative muscle recruitment strategy. Thus, muscular force measurement becomes important in gait.


An example of sensors which detect the muscular force during static posture or dynamic movements is electromyography sensors.


11.3.1.1. Working principles (Zheng et al. 2022)


Electromyography is a device which uses electrodes to pick up electrical signals from the muscle during its contraction and sends the impulse to the machine. The machine records the signals in detail and projects the information in graph form as an electromyograph. The signals are detected by the electrical pads placed on the area of contact through a velcro strap. Aqua Gel is placed between the electrode and the skin in order to reduce artifaction. Hence, these electric pads are called EMG sensors. Muscular contraction generates an electrical signal, or EMG signal, that can be detected by the EMG sensor.


The recordings vary according to the phases of EMG. Figure 11.9 shows the diagrammatic representation (Wang et al. 2021).

A flow diagram illustrates electromyography signal patterns during various muscle contraction phases with muscle and electrode placement.

Figure 11.9. Electromyography (EMG) signal patterns during different muscle contraction phases (Wang et al. 2021)


Figures 11.10–11.12 demonstrate the position of the selected muscle and the reference electrodes (Wang et al. 2021).

Two photos depict a device labeled BIO-S connected to a person's leg, highlighting muscles including rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and a reference electrode.

Figure 11.10. Positioning of selected muscle and placement of reference electrodes for EMG measurement

A diagram illustrates the placement of E M G electrodes on the trunk, thigh, and calf for muscle activity detection.

Figure 11.11. Placement of EMG electrodes for upper or lower limb muscle activity detection

A three-dimensional graph illustrates recognition rates against test ratios and shapes, with multiple data points connected by lines.

Figure 11.12. Example of EMG electrode placement for gait analysis


11.3.1.2. Role of force measurement sensors in physiotherapy


Because they provide precise, real-time data that aids physicians in evaluating, tracking and optimizing the recovery process, force measurement sensors are essential to physiotherapy. Both the patient and the physiotherapist can benefit from these sensors’ ability to monitor the force or pressure used during particular movements or exercises. They support physiotherapy in the following ways:



  1. Objective assessment of force and strength: force sensors enable physiotherapists to accurately quantify the strength or force a patient generates during exercises, joint movements or muscle contractions, offering a more objective and precise measurement compared to subjective evaluations such as verbal descriptions of pain or effort (Hollander et al. 2021). For instance, they can assess muscle strength by measuring the force exerted during exercises, helping identify weaknesses critical for conditions such as stroke rehabilitation, orthopedic recovery or sports injuries. Additionally, force sensors can evaluate joint stability by assessing the load a joint can bear, which is essential for tracking recovery after surgery or injury.
  2. Personalized treatment plans: with detailed force data, physiotherapists can tailor treatment plans based on the individual’s specific strengths and capabilities. Sensors can provide insights into which exercises the patient struggles with, or where they may be overexerting themselves (Rossi and Gatti 2020). This helps adjust the intensity or focus of rehabilitation programs for more effective outcomes.
  3. Monitoring progress: force measurement sensors track a patient’s progress over time by documenting force values during various movements or exercises, which is especially useful in rehabilitation after surgery or injury. For example, after knee surgery, sensors can monitor improvements in the patient’s ability to bear weight or apply force through the leg (Ng and Lee 2018). They are also valuable in managing chronic conditions such as arthritis or neurological disorders, as they help assess changes in muscle strength and joint function. These data allow physiotherapists to make informed adjustments to the therapy plan based on the patient’s progress or setbacks.
  4. Preventing overexertion and injury: pushing the body too hard or too rapidly is one of the main hazards in rehabilitation. By preventing patients from going beyond their limit, force sensors reduce the chance of additional harm (Almeida and Vieira 2020). Therapists can help patients do exercises safely and within their physical limits by establishing force limits and monitoring real-time effort.
  5. Feedback for patient motivation and engagement: real-time feedback from force sensors can be provided to patients, allowing them to understand how much force they are exerting and how they are progressing in their rehabilitation. This immediate feedback helps boost motivation, as patients can see tangible improvements, such as increased force or pressure tolerance during specific exercises (Kim and Lee 2019).
  6. Improved biomechanical analysis: for more complex assessments, force sensors can be integrated into wearable devices or mats to track the distribution of force across the body during activities such as walking, standing or other movements. This integration helps therapists analyze gait abnormalities by revealing how force is distributed, which can highlight posture or alignment issues that can be addressed through targeted therapy (Ding et al. 2020). Additionally, in balance training, sensors assess the force exerted by each limb or on different sides of the body, helping to develop a balanced rehabilitation approach and improve coordination.
  7. Evaluation of functional movements: force sensors can be integrated into tools such as pressure mats, force plates or wearable devices to evaluate functional movements (e.g. squatting, standing up from a chair, etc.) (Molenaar et al. 2015). They help identify whether the patient is compensating during movements or engaging muscles inefficiently, which is useful for both rehabilitation and performance optimization.
  8. Posture and alignment assessment: improper posture can lead to muscle imbalances and chronic pain. Force sensors can help measure how the body distributes weight while standing or sitting, identifying any asymmetries or improper alignments (Cavagna and Bianchi 2016). These data help create a treatment plan aimed at correcting posture and preventing future musculoskeletal issues.

11.3.1.3. Artificial intelligence integration in force measurements



  • Objective assessment of force and strength:

    • AI integration: machine learning models analyze real-time data from force sensors to identify specific patterns in force output, helping to quantify muscle strength and joint stability.
    • Basic AI algorithm: linear regression or support vector machines (SVMs) can be used to model and predict muscle strength based on force data (Rana et al. 2020).

  • Personalized treatment plans:

    • AI integration: AI analyzes the force sensor data over time to understand the patient’s capabilities, detecting weaknesses or areas of overexertion. This helps tailor the therapy to each individual’s specific needs.
    • Basic AI algorithm: decision trees or K-means clustering can group patients based on force output patterns, recommending personalized exercises or intensity levels for each group (Soni and Gupta 2020).

  • Monitoring progress:

    • AI integration: AI algorithms track force measurements over time, comparing them to baseline data to assess progress, improvements or setbacks in rehabilitation.
    • Basic AI algorithm: time-series analysis (using algorithms such as ARIMA) for continuous tracking of force values and identifying trends over time (Vasudevan and Arjunan 2019).

  • Preventing overexertion and injury:

    • AI integration: AI predicts potential risks by analyzing the real-time force exertion data and comparing it to safe limits, adjusting intensity to prevent overexertion.
    • Basic AI algorithm: anomaly detection algorithms such as isolation forests or K-means clustering can help detect when a patient is approaching unsafe force thresholds (Reddy and Suresh 2020).

  • Feedback for patient motivation and engagement:

    • AI integration: AI provides real-time feedback based on force sensor data, presenting patients with progress visualizations or motivational messages, and adjusting the difficulty of exercises based on performance.
    • Basic AI algorithm: reinforcement learning (RL) could be used to adjust exercise intensity dynamically based on patient performance and feedback (Singh and Gupta 2021).

  • Biomechanical analysis:

    • AI integration: AI analyzes force distribution across different parts of the body, identifying abnormal gait patterns or imbalances that need attention.
    • Basic AI algorithm: deep learning (convolutional neural networks, CNNs) can process sensor data from wearable devices to detect gait abnormalities or posture misalignments (Venkatesh and Sharma 2020).

  • Evaluation of functional movements:

    • AI integration: AI uses sensor data to assess whether a patient is compensating or performing movements inefficiently, such as during squats or standing exercises.
    • Basic AI algorithm: recurrent neural networks (RNNs) or long short-term memory (LSTM) networks can analyze time series data from force sensors to evaluate functional movement patterns (Prakash and Ghosh 2021).

  • Posture and alignment assessment:

    • AI integration: AI algorithms detect weight distribution imbalances or misalignments in posture, helping to correct alignment and prevent musculoskeletal issues.
    • Basic AI algorithm: random forests or SVMs can be used to classify posture alignment based on sensor data and recommend corrective actions (Kumar and Singh 2020).

11.3.2. Pressure measurement


Pressure measurement in physiotherapy is essential for assessing and optimizing a patient’s biomechanical performance during rehabilitation. By measuring pressure distribution across the body, such as under the feet, back or other body parts, it means physiotherapists can analyze how forces are transmitted through the body during movements such as standing, walking or exercising. This helps identify areas of excessive pressure or insufficient load-bearing, which can indicate improper posture, misalignment or muscle imbalances. For example, an uneven distribution of pressure across the feet may suggest gait abnormalities, while concentrated pressure on certain body parts can indicate incorrect posture or compensation patterns. Real-time feedback from pressure sensors helps patients adjust their movement patterns, ensuring that forces are applied efficiently, minimizing the risk of injury and improving overall biomechanics (Patel and Patel 2020). Pressure data also assists in tracking the progress of joint mobility or strength after injury or surgery, providing insight into how the body is adapting to stress over time. In cases of conditions such as stroke, arthritis or paralysis, pressure measurement can help identify areas at risk for pressure ulcers or sores, prompting early interventions to prevent such injuries. By enabling precise, data-driven adjustments to rehabilitation programs, pressure measurement helps improve movement efficiency, stability and comfort, ultimately promoting safer and more effective recovery.

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Mar 15, 2026 | Posted by in ONCOLOGY | Comments Off on AI-driven Sensor Technologies in Physiotherapy and Oncology: Transforming Rehabilitation Through Intelligent Biomechanical Monitoring

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