PLMM_Touch Data Logger Used to Determine That A Robot Can Prevent Osteoporosis Following an Acquired Brain Injury
It is recognised that weight-bearing exercise is important for preventing osteoporosis in stroke patients. However, the immobility of many stroke patients often makes it difficult for these individuals to benefit from this. In recent years advances in robot-assisted therapeutic devices have provided an innovative way to potentially overcome some of these limitations.
These developments have been important as robot-driven therapy can accurately and objectively measure patient’s joint kinetic and kinematic outcomes and gait patterns. In addition, its endurance is unaffected by repetitive tasks, and therefore human error can be avoided. However, the effects of this therapy on osteoporosis prevention have not been fully understood.
Most importantly, it is essential that the weight going through the limb has the same effect on bone regrowth as if the patient was walking naturally heel toe. Weight-bearing measurement is essential during physical activities of stroke survivors, and whilst ambulatory devices, such as pressure/force insole, have been used to assess ground force reaction (GFR) and peak ground force reaction (PGFR) the integration of robots with ambulatory devices in neuro-rehabilitation may lead to better outcomes for preventing osteoporosis.
Research to determine the relationship between weight bearing exercises conducted using robot-assisted devices and their induced biomechanical stimuli on bone is limited and, as a consequence, the successful design of training programs much depends on the clinician’s experience rather than understanding the underlying relationship.
nCounters’ Portable Limb Load Monitor, the PLMM_Touch Data Logger has been designed to assist therapists to objectively verify whether a patient is loading the appropriate percentage of body weight through the affected limb. It is intended to enhance, not replace existing therapies. To prove its effectiveness, we participated in a detailed research project with the University of Melbourne.
Testing The PLMM_Touch
nCounters Engineering Rehabilitation has a long-standing history of collaboration with Associate Professor Lihai Zhang. Working together we collaborated on a series of clinical trials designed to evaluate the efficacy and effectiveness of robot-assisted weight bearing exercises for stroke patients using the PLMM_Touch Data Logger. This work was carried out at the Melbourne-Shenzhen Rehabilitation Research Centre, Shenzhen, China.
The study measured vertical GRFs using our force insoles, with two force sensors attached to the forefoot and heel respectively. A Data Logger was connected to these insoles and placed above each subject’s right ankle. The sampling rate was 100 Hz and a Fast Fourier Transform technique was used to filter background noise. The data is recorded on a micro SD card. A customized Excel program which is provided with the Data Logger was used to graph the GRF-time history curves.
The computational results suggested that a static robot can be used to reduce the risk of bone resorption, and hence prevent development of osteoporosis for motor impaired patients.
The GRF data that was collected and analysed from both heel and forefoot pads used in the study produced consistent and reliable results. The outcomes of our collaborative research work have also resulted in two peer-reviewed manuscripts.
“All of the vertical GRF–time curves exhibited the classic patterns of walking with double peaked configuration. Most importantly, our work has been translated into the first National Rehabilitation Standard of China (GB/T37103-2018)” Assoc. Professor Lihai Zhang University of Melbourne – See below
nCounters Engineering Rehabilitation is a Melbourne-based Australian company that develops, designs, and manufactures innovative electronic biofeedback and treatment devices for rehabilitation clinicians, physiotherapists, and occupational therapists. Our products are designed to aid in the management of gait, balance, and movement for patients following traumatic brain injuries, such as stroke, or lower leg amputations and other orthopaedic surgeries.
Vertical GRF -time histories for a series of walking tests on robot
Vertical GRF – time histories for a series of walking tests on the treadmill
Peter designs and builds biofeedback products for gait and movement in the orthopaedic and stroke rehabilitation spaces. These devices are programmed for ease of use with built in help touch screens. In all cases data is transferred wirelessly to ensure patient safety.They can store and display data in real time so as to monitor the patient’s overall progress.