Development of an Embedded Device for Quantifying and Recording Daily Standing Profiles in Individuals with Lower Limb Motor Impairment Using an Assistive Standing Mobile Device ^†

Abstract

The present study introduces an innovative device designed to objectively record and quantify the daily standing profiles of individuals with lower limb motor impairment. The device is specifically developed to be seamlessly embedded onto the standing platform of an assistive standing mobile device, without compromising its structural integrity or functional capabilities. The primary objective of this device is to provide objective evidence of patients’ standing activities within their home environment, thus facilitating the assessment of patient performance and usage. The embedded device captures and stores comprehensive data regarding the duration, frequency, and interval of patients’ standing sessions. Furthermore, the device integrates wireless connectivity to facilitate data transfer and analysis. The development process involved close collaboration between rehabilitation engineers and physiotherapists to ensure optimal functionality, user-friendliness, and unobtrusiveness. Extensive testing and validation procedures were conducted to assess the reliability, validity, and feasibility of the device. The results demonstrate its high accuracy and reliability in capturing and quantifying standing profiles. The proposed device addresses a critical need within the field of rehabilitation, providing clinicians, researchers, and funding organizations with objective evidence of patients’ standing abilities and adherence to rehabilitation protocols. This evidence-based approach has the potential to enhance clinical decision making, improve treatment outcomes, and secure financial support for patients in need of assistive standing mobile devices. In conclusion, the embedded device presented in this study offers a novel and practical solution for quantifying and recording the daily standing profiles of individuals with lower limb motor impairment. By providing objective evidence of patients’ standing activities, this device has the potential to advance the field of rehabilitation and facilitate improved access to assistive standing mobile devices for those in need.

1. Introduction

1.1. Background

Lower Limb Motor Impairment is one type of disability in which individuals lose part or whole coordination of lower limbs caused by stroke, spinal cord injuries, or cerebral palsy. Patients experiencing this medical condition have to deal with difficulty in standing and walking. In the scope of lower limb motor training, robotic lower limb exoskeletons are commonly adopted in rehabilitation clinics for user-driven or passive gait training to enhance proper gait patterns, especially in young patients [1]. For senior adults and patients with upper body weakness, exoskeletons are not applicable due to their difficulties in maintaining body balance in upright posture. Although those patients are prohibited from walking due to restriction in operating lower limb exoskeletons, standing training is physiologically beneficial to reduce the risk of pressure sores, improve gastrointestinal function, strengthen bone density, etc. [2]. To help patients perform standing training, numerous standing training aids have been launched in the market such as standing frames and assistive standing mobile devices, robotic devices assisting patients in performing standing and sitting.

Traditional facility-based rehabilitation models provide important clinical supervision but can be resource intensive [3]. Accordingly, community-based rehabilitation has emerged as a cost-effective alternative to promote patient independence and functional recovery in real-world environments [4]. A recent study showed that home-based rehabilitation can effectively enhance functional and emotional recovery of various disabilities, including stroke and spinal cord injuries [5,6,7]. Well-planned remote rehabilitation programs have become acceptable by patients as they reduce their time in traffic and follow-up consultations, especially after the pandemic period. Therefore, home-based rehabilitation is not only beneficial to patients but is also treated as an alternative to traditional clinic-based therapies due to the reduced cost, time, manpower and capital.

However, home-based rehabilitation is not meant to be a let-go policy, and delivering rehabilitation programs remotely presents challenges in objectively monitoring patient compliance. According to the National Library of Medicine, about 40% of patients failed to follow the medical advice, including prescribed treatments [8]. To be a responsible healthcare service provider, the procedure and progress of rehabilitation programs are required to be monitored in order to understand the situation of patients. Phone interviews and home visits are currently the most common methods to monitor the progress of recovery. Patients may fail to accurately report participation in training by lying, false memory, or memory loss. The progress of the rehabilitation programs is hindered, which possibly causes deterioration of lower limb ability and development of physiological disorders such as osteoporosis or kidney malfunction in future.

Therefore, in this study, we propose the implementation of a Standing Tracking system to tele-monitor the rehabilitation progress of patients who are prescribed standing training programs with assistive standing mobile devices. An additional embedded device was developed to record the time spent in standing training and to then upload standing data to a cloud spreadsheet. To facilitate the upload process, a 4G mobile data communication module is integrated to the system, allowing data transfer in residential and community environments. When patients step on the platform of the assistive standing device, the Standing Tracking system is started.

1.2. Related Work

There is a lack of specific research on the development of compliance monitoring about standing tasks with assistive standing devices. However, previous review studies have emphasized major considerations about the development and application of IoT services in rehabilitation [9]. Researchers have proposed that, in terms of system design, the ease of the system should be first considered since the tracking system is aims to provide timely feedback to patients [10]. Meanwhile, the system should be compatible with problems to be solved and patients with different proficiency in technology in order to avoid overuse of technology.

Devices and equipment were also recommended to be considered according to the system design, including the device for collecting, processing, and sharing data and information. Communication protocol was required to be considered according to the range of data transmission [11]. Additionally, understanding the methodology of the rehabilitation program was needed to develop an appropriate IoT system in order to promote significant system usability [12]. The studies also proposed that sensors should be included in IoT systems to capture the movement of patients. However, sensors should give minimum influence on patients’ actions. Sensors are expected to be ignored by patients even if sensors are detecting the activities of rehabilitation training [9].

2. Method and Material

The Standing Tracking system consists of 4 main components (Figure 1), including the microcontroller unit, 4G module, inertia measuring unit (IMU), and foot switch module. When the patient’s weight is applied to the foot switch, the microcontroller would be powered up, as well as the 4G module and IMU. The 4G module starts initialization to connect to the 4G mobile data network. While the IMU is initialized to acquire the tilting angle. Then, the tilting angles data are uploaded to a cloud-based spreadsheet and are available to be reviewed by therapists.

2.1. Components

2.1.1. Microcontroller Unit

In the tracking system, Arduino Mega 2560 (Arduino, Ivrea, Italy) is used as a microcontroller unit which distributes power to, initializes, and communicates with peripheral devices. The embedded microprocessor is ATmega2560 (Microchip Technology, Chandler, AZ, USA), which can operate with 16 MHz in maximum clock speed. The microprocessor also includes 8 KB of RAM, allowing processing of 2000 floating-point numbers per operation, which suits for acquiring 3-axis acceleration measurements from the IMU. A lithium rechargeable battery module is used as a power source for supplying the microcontroller unit. The program for data acquisition and transmission is flashed in the Microcontroller unit for operation.

2.1.2. 4G Module

A 4G module is required to establish the connection between the cloud service and local data acquisition device. SIM7000C NB-IOT shield (DFROBOT) is used in the tracking system because it is compatible with the frequency band used for telecommunication in the Asia region and it is compatible with the microcontroller unit for data flow and operation. Hayes AT command set (AT command) is the major command language for operation, which can be sent by the microcontroller unit through Universal Asynchronous Receiver/Transmitter (UART) protocol. Meanwhile, a SIM Card with registered 4G mobile data service is required for activating the module.

2.1.3. Inertia Measuring Unit (IMU)

Besides standing training, general assistive standing mobile devices offer sitting assistance to patients to avoid exhaustion. Therefore, the tilting angle of the upper body arm is a necessary indicator of standing. In the tracking system, a GY-521 6-axis IMU is included for tilting angle measurement (Figure 2). The tilting angle is derived from measuring 3 axes translational acceleration values. In terms of geometry, the tilting angle is described by pitch angle, which is defined as the angle between the Z-axis and axis of gravity. The conversion of pitch angle and linear acceleration is

$$Pitchangle={tan}^{-1}\frac{\sqrt{A_x^2+A_y^2}}{A_z}$$

(1)

The conversion is only valid if gravity is the only acceleration exerted on the IMU. Since standing mobile devices are prescribed for standing training only, angle calculation during driving, vibration, or collisions will be neglected.

2.1.4. Foot Switch Module

The foot switch module, installed near the standing platform, is used to trigger the tracking device. The module consists of 2 ends, which are connected to the power source and power input pin of the Arduino Mega 2560, respectively. When patients are shifted to standing mobile devices, the patients’ weight is applied on the foot switch module, and the Arduino Mega 2560 is powered on. For effectively capturing the applied weight, the foot switch module includes an array of mechanical switches which are connected in parallel. When either one or more switches are pressed, the tracking device will be powered on.

2.1.5. Power Module

To power up the tracking device, 2 parallel connected 18,650 Lithium rechargeable batteries (2400 mAh at 3.7 V in total) are used as sources of power. A DC-DC transformer is installed to step up the supply voltage to 9 V for the input of the microcontroller unit and 4G module. The device consumes 9 V 100 mA in the data acquisition and processing period (80% time of cycle in average), while it consumes 9 V 140 mA while sending HTTP requests with the 4G module (20% time of cycle in average). Battery discharging tests have been conducted, and it is observed that 6 h of continuous usage is supported by the power module. For convenience of patients and caregivers, the battery module is charged by a 5 V DC power supply, which is common in domestic chargers for smartphones.

2.2. Mechanism

To start the standing training, the patient steps on the standing platform as well as the foot switch module. The battery module then powers on the microcontroller unit and 4G module. The Microcontroller unit sends AT commands to the 4G module to search for and register the mobile data station according to the mobile data service provider.

Meanwhile, the IMU is initiated to continuously acquire the 3 axes of acceleration data and computes the pitch angle according to Formula (1). The microcontroller unit then commands the 4G module to a HTTP POST request, which includes pitch angle data in the form of the JSON structure, and sends this to webhook. Once the request is received, webhook can automatically relay the data and additional timestamps to the cloud spreadsheet. By using this network pathway, all data are directly streamed to a cloud spreadsheet and no local data storage is included in the system (Figure 3). Since all the configurations are performed before intervention, no extra settings are required after installation and distribution of the tracking device.

2.3. Data Validation

Data validation is crucial in tele-monitoring systems since it serves as an indicator of data corruption. It is expected that the value of IMU measurement is in the range of 0–90°, which is the range of movement of the upper body arm of the assistive standing mobile device. Therefore, any values out of this range or null values would be considered invalid data. As a result, corrective maintenance would be conducted once null values are received in order to reduce the disturbance of the rehabilitation program.

3. Conclusions and Discussion

The main contribution of this study is the development of a standing tracking system integrated with assistive standing mobile devices and the establishment of a connection between the system and cloud spreadsheet. The proposed system adopts the idea of the Internet-of-Things (IoT), providing a tele-monitoring function for device utilization and compliance of patients with prescribed home-based rehabilitation training. The system provides objective data for healthcare professionals to understand the progress of rehabilitation programs, and instant feedback can also be provided to patients as encouragement. Additionally, there are no extra operation requirements for the tracking device except recharging the battery module, which suits patients with different levels of proficiency in technology and reduces the risk of operation failure. The ultimate goal of this research is to enhance the connection among the assistive rehabilitation devices and the internet, adapting the IoT on medical devices to promote tele-healthcare and rehabilitation near residential communities.

3.1. Compliance of Prescribed Intervention

An effective rehabilitation program requires cooperation between patients and healthcare professionals. On the one hand, healthcare professionals have to provide adequate instruction and feedback to patients, while on the other hand, patients have to follow those instructions and perform the corresponding exercises. By installing the tracking device system, the usage will be comprehensively uploaded to the cloud spreadsheet, which can significantly improve the efficiency of data collection and eliminate human error during progress evaluation. With accurately collected data, more appropriate interventions can be provided to patients.

3.2. Privacy

Privacy is a major concern of patients or caregivers when adopting tele-monitoring systems. To solve this conflict, clinical professions should regularly share the standing records with patients and caregivers accordingly. Therapists can illustrate the progress and improvement using the acquired data to patients as positive feedback, which can pin a virtual target on patients psychologically. It is important to note that patient data will not be streamed to the cloud. Only standing profile information, such as duration and standing angle, will be accessible on the cloud. To maintain confidentiality, each patient will be assigned a unique code, allowing us to match their demographic information at the backend.

3.3. Accessibility

The intended users of the device are individuals with lower limb motor impairment who are participating in rehabilitation training, particularly standing exercises prescribed by clinical professionals. This device serves as tele-rehabilitation equipment capable of monitoring patient compliance and capturing their standing profile over a specified period.

4. Limitation and Future Development

The foot switch module is a binary sensation module which considers only whether patients are performing standing training with assistive standing mobile devices. To obtain a comprehensive profile of the standing rehabilitation program, the distribution of weight bearing is also a key feature for indicating the quality of standing training. Uneven distribution of weight causes unequal force acting on tissues due to gravity, which causes extra pain, deformity, and contracture [4]. Therefore, a pressure sensor will be considered as an additional sensor in future improved versions, which could provide a description of the weight bearing symmetry. Feedback about symmetric standing posture would be available for patients in order to reduce the risk of injury and bone deformities.

Apart from additional pressure sensors, future work will also include recruiting patients with lower limb impairment to validate the standing tracker tele-monitoring system. Representing a major distribution of lower limb impairment, the stroke group will be the first stage of pilot study and it will then expand to other groups such as those with spinal cord injury. In preparation for the upcoming pilot trial involving patients, a formal research study will be conducted. Ethical approval will be sought from the hospital ethics committee to ensure compliance.